DPV-SLAM++ on KITTI Odometry seq 00 + 09 with trajectory polyline overlaid on Karlsruhe Google Earth aerial

Goal

Approach

What went wrong

Code plan

1. 01Step 0 [sandbox bootstrap + host pre-flight] — ✓ PREFLIGHT PASSED (iter 1): GPU sm_86 (RTX 3080 Laptop, 16384 MiB) confirmed on host via nvidia-smi AND inside a `--gpus all` container (cached nvidia/cuda:12.4.0-base-ubuntu22.04) — container GPU passthrough (the M-006 trap) works. nvcc absent on host is expected (CUDA-devel base supplies it at build time; gates in Step 1). CUDA-IPC smoke non-gating per spec. The literal torch-on-host preflight_command is the wrong layer (torch lives in container); gating intent satisfied. ✓ SKELETON EXISTS (mission-skill bootstrap): CLAUDE.md, CHANGELOG.md, src/tests/runs .gitkeep, and pillar algorithm/vision-robotics/CLAUDE.md all present. REMAINING Step 0 work (first executing transition): author docker-compose.yml + Dockerfile at sandbox root with `gpus: all` under both build AND deploy.resources (host-first per feedback memo). Base FROM nvidia/cuda:12.4.0-devel-ubuntu22.04 (devel = nvcc present; matches the cached base tag); set ENV TORCH_CUDA_ARCH_LIST=8.6 so lietorch/DPVO CUDA kernels compile for the 3080. Defer exact torch+cuXXX wheel pin to Step 1 (read upstream README). Files: docker-compose.yml, Dockerfile. ~~✓ DONE (iter 2)~~: both files authored at sandbox root — Dockerfile (FROM nvidia/cuda:12.4.0-devel-ubuntu22.04, ENV TORCH_CUDA_ARCH_LIST=8.6 + WANDB_MODE=disabled, eigen3/GL system deps, commented Step-1 install hook) + docker-compose.yml (service `vision-robotics` matching the oracle, host-first `build.gpus: all` + runtime GPU reservation, `.:/workspace` bind-mount, NO ipc:host since single-process). Step 0 fully complete; Step 1 (clone DPV-SLAM + author install_dpvslam.sh + build GREEN) is next.
2. 02Step 1 [DPV-SLAM++ env + CUDA-IPC smoke test] — clone the DPV-SLAM repo at a pinned commit SHA (read upstream README for the recommended one; record the SHA in CHANGELOG for v3-C determinism). Author src/setup/install_dpvslam.sh (clone + submodules + torch+cu118 + DPV-specific deps; --no-build-isolation when installing CUDA-kernel submodules per M-006 Trap #5). Add src/setup/probe_no_cuda_ipc.py that smoke-tests `torch.multiprocessing.spawn` IS NOT used by importing DPV-SLAM's top-level module and confirming no `mp.Process` with CUDA tensors is spawned (read-only static check via grep, not a runtime probe). Run `docker compose build vision-robotics` and verify GREEN. Files: src/setup/install_dpvslam.sh, src/setup/probe_no_cuda_ipc.py, Dockerfile (if upstream's is unusable), runs/build_iter<N>.log. ~~✓ CODING DONE (iter 3)~~: single-process GATE verified upstream BEFORE pinning (princeton-vl/DPVO @ 859bbbfdac6c6185f345003b3c473901fcd13ace — mp call sites exist but every one passes only .cpu()/numpy across the boundary; long_term.py fences the PGO Pool with `.Inv().cpu()`; NO live CUDA tensor crosses a process → does not hit the M-006 WSL2 CUDA-IPC wall). Authored install_dpvslam.sh (torch 2.3.1+cu121 per upstream environment.yml, torch_scatter, --no-build-isolation CUDA-ext + DBoW build, dpvo.pth + ORBvoc.txt + kornia-cache baked in, skips Pangolin DPViewer, GPU-free import sanity), probe_no_cuda_ipc.py (deterministic static audit = build GATE, v3-C), and wired both into the Dockerfile. Corrected 3 upstream traps in CLAUDE.md (KITTI uses image_2 COLOR not image_0; ORBvoc.txt abs-path; kornia hidden net dep). REMAINING Step-1 work (next executing transition): run `docker compose build vision-robotics` to GREEN, tee → runs/build_iter3.log. ~~✓ COMPOSE FIX (iter 5)~~: removed `build.gpus: all` (Trap #5 — host Compose v2.37.1-desktop.1 hard-rejects the key); `docker compose config -q` now clean. The image build itself has NOT run GREEN yet — that is the next executing→executing transition (logs → runs/build_iter5.log). ~~✓ BUILD STARTED + WGET TRAP (iter 6)~~: build ran for real — base pull, apt layer, torch 2.3.1+cu121 + torch_scatter + python deps, DPVO clone @ 859bbbf ALL GREEN — then died at install_dpvslam.sh L64 `wget: command not found` (exit 127); the cuda-devel base ships neither wget nor unzip (Trap #6). ~~✓ WGET FIX (iter 7)~~: added `wget unzip` to the Dockerfile apt-get list (1-file edit). The nvcc sm_86 CUDA-extension compile + the probe_no_cuda_ipc.py BUILD GATE remain UNPROVEN — next executing→executing runs `docker compose build vision-robotics` to GREEN, logs → runs/build_iter7.log. ~~✓ DPVO CUDA COMPILE GREEN + OPENCV TRAP (iter 8)~~: build_iter7.log proved the dpvo nvcc sm_86 wheel BUILDS GREEN (~231s) — biggest unknown retired — then died at the `dpretrieval` (DBoW) wheel: `DPRetrieval/CMakeLists.txt:3 find_package(OpenCV)` failed (no system OpenCV; the python opencv-headless wheel ships no cmake config). Trap #7. ~~✓ OPENCV FIX (iter 9)~~: added `libopencv-dev` to the Dockerfile apt list (1-file edit). Two stages remain UNPROVEN — the dpretrieval C++/cmake wheel (now that OpenCV is present) and the probe_no_cuda_ipc.py BUILD GATE (never reached). Next executing→executing runs `docker compose build vision-robotics` to GREEN, logs → runs/build_iter8.log. ✗ BUILD FAILED + DBoW2 TRAP (iter 10): build_iter8.log BUILD_EXIT=1 — the Trap #7 libopencv-dev fix WORKED (`-- Found OpenCV: /usr (found version 4.5.4)`, log L2603) but dpretrieval's CMakeLists.txt:6 does a SECOND `find_package(DBoW2 REQUIRED)` → `Could not find DBoW2Config.cmake` (Trap #8). DPVO bundles DBoW2 as a top-level submodule (lahavlipson/DBoW2) that `clone --recursive` fetches but nothing builds/installs. Reverted executing→planning. FIX (next planning→executing, install_dpvslam.sh 1-file edit, NO new apt dep — DBoW2 needs only the already-present OpenCV, DLib vendored): insert between current steps 7 and 8 `cd $DPVO_DIR/DBoW2 && mkdir -p build && cd build && cmake .. -DCMAKE_BUILD_TYPE=Release && make -j$(nproc) && make install && ldconfig` → writes DBoW2Config.cmake to /usr/local/lib/cmake/DBoW2/ where DPRetrieval looks. probe_no_cuda_ipc.py BUILD GATE STILL unexercised (last stage after DPRetrieval). ~~✓ DBoW2 FIX CODED (iter 11)~~: inserted step 7b in install_dpvslam.sh between the DPVO wheel (step 7) and the DPRetrieval wheel (step 8) — `cd ${DPVO_DIR}/DBoW2 && mkdir -p build && cd build && cmake .. -DCMAKE_BUILD_TYPE=Release && make -j$(nproc) && make install && ldconfig` (1-file edit, no Dockerfile/apt change — OpenCV already present, DLib vendored). Next executing→executing runs `docker compose build vision-robotics` to GREEN, logs → runs/build_iter9.log; the dpretrieval wheel (OpenCV+DBoW2 now both satisfied) and the probe_no_cuda_ipc.py BUILD GATE are the two remaining unproven stages. ✗ BUILD FAILED + PROBE FALSE-POSITIVE (iter 12): build_iter9.log proved BOTH remaining upstream stages GREEN — `Successfully built dpvo` (L401) AND `Successfully built dpretrieval` (L461); Traps #7+#8 retired, the whole C++/CUDA chain works. Build then hit our OWN probe_no_cuda_ipc.py gate (first time reached) → BUILD_EXIT=1. The REAL invariant PASSED (`cpu_fence_ok: true`, no missing fences — no live CUDA tensor crosses a process boundary); only the secondary 'no unaudited mp call site' tripwire fired, over-collecting 6 NON-RUNTIME files: build/lib.*/dpvo/{dpvo,stream}.py (setuptools artifact copies of audited source), Pangolin/scripts/vcpkg/.../generatePortVersionsDb.py (GUI/build tooling, never imported), and evaluate_{euroc,icl_nuim,tum}.py (sibling eval scripts for datasets we don't run — verified read-only at SHA 859bbbf to be structurally identical to the audited evaluate_kitti.py: Process(*_image_stream) reader, .cpu().numpy() frames over a Queue, .cuda() only in the MAIN process after queue.get()). FALSE POSITIVE in our own gate, NOT the CLAUDE.md 'STOP do-not-fix' case (which needs cpu_fence_ok:false). Reverted executing→planning. FIX (next planning→executing, 1-file edit to src/setup/probe_no_cuda_ipc.py, v3-D): (a) extend iter_py_files to also skip `build/` + `Pangolin/` (alongside existing pybind11/DPViewer/DPRetrieval skips); (b) add evaluate_{euroc,icl_nuim,tum}.py to AUDITED with the verified CPU-Queue rationale (tripwire stays honest). Then re-run → runs/build_iter10.log; probe is a late layer so torch+dpvo+dpretrieval stay cached → re-run reaches the probe in seconds. This is the LAST build blocker. ~~✓ PROBE FIX CODED (iter 13)~~: applied the Trap #9 fix to src/setup/probe_no_cuda_ipc.py (1-file, coding-only) — (a) iter_py_files now also skips build/ (setuptools artifact copies) + Pangolin/ (GUI/build tooling), (b) added evaluate_{euroc,icl_nuim,tum}.py to the AUDITED allowlist with the verified CPU-Queue rationale; the regression tripwire stays honest (a NEW runtime mp call site or a dropped long_term.py .cpu() fence still fails). The REAL invariant already passed at iter 12 (cpu_fence_ok:true), so this is NOT the CLAUDE.md STOP case — only the secondary completeness walk was over-collecting. REMAINING (next executing→executing): run `docker compose build vision-robotics` to GREEN, logs → runs/build_iter10.log; probe is a late cached layer so the re-run reaches it in seconds. ~~GREEN (iter 14)~~: ran `docker compose build vision-robotics` -> BUILD_EXIT=0, image blueberry-m007-dpvslam:latest built+named (build_iter10.log L110-117). Probe GATE PASSED: single_process_cuda_safe:true, unaudited_call_sites:[], cpu_fence_ok:true, [probe_no_cuda_ipc] PASS. Whole env chain proven (torch 2.3.1+cu121, dpvo nvcc sm_86 wheel, dpretrieval DBoW C++ wheel, DBoW2 install, probe gate); Traps #5-#9 ALL retired. STEP 1 COMPLETE -- Step 2 (KITTI color download) is next.
3. 03Step 2 [KITTI seq 00 + 09 download] — src/setup/download_kitti.py: download KITTI Odometry monocular grayscale dataset (left image_0 only, sequences 00 and 09 — ~8 GB total). Use the KITTI server's published checksums; cache the .zip under runs/data/ (NOT committed — sync_to_public excludes runs/data per the M-006 lesson). Extract to runs/data/kitti/sequences/{00,09}/. Also fetch the ground-truth pose files (poses/00.txt, 09.txt — small text). Files: src/setup/download_kitti.py, runs/data/kitti/ (gitignored). ~~✓ CODING DONE (iter 15)~~: authored src/setup/download_kitti.py — CORRECTS the original plan's grayscale image_0 to COLOR image_2 (Trap #1). Resolved a real disk-budget conflict: the official Odometry color distro is a monolithic ~65 GB `data_odometry_color.zip` that cannot be sliced and blows `disk_gb: 12`; instead assembles seq 00/09 from the KITTI **raw** per-drive `_sync` archives (00=2011_10_03_drive_0027/4541, 09=2011_09_30_drive_0033/1591 — published full-drive mapping), extracting only `image_02/data`, deleting the raw zip after (disk budget), and pairing with the OFFICIAL small Odometry poses+calib zips (~5 MB) for the evo Sim3 oracle. Stdlib-only (urllib+zipfile, no wget/unzip dep), atomic `.part` downloads, idempotent, Trap #2 12-col GT guard, machine-checkable frame-count verification. Also added sandbox `.gitignore` (runs/data, MEASURED-*/tiles) — runs/data was NOT previously ignored. REMAINING Step-2 work (next executing→executing RUN transition): `docker compose run --rm vision-robotics python src/setup/download_kitti.py` — this is where raw S3 URL liveness + the full-drive frame-count assertions are actually exercised; on failure it reverts executing→planning. ✗ RUN FAILED (iter 16): S3 live, poses/calib correct (00=4541, 09=1591), raw drive 0027 extracted 4544 frames but seq 00 GT expects 4541 → frame-count assert fired (Trap #10: odometry seq is a frame-RANGE SUBSET of the raw drive, not the full drive; drive 0027 zip is 18.35 GB not ~4.7 GB). Reverted executing→planning. ~~✓ FIX CODED (iter 17)~~: 1-file edit to download_kitti.py — SEQ_TO_RAW now carries per-seq range (00: 0..4540; 09: 0..1590), fetch_sequence_images slices all_frames[start:end+1] and asserts len(all_frames) >= end+1 instead of full-drive equality; docstring corrected (range subset + 18.35 GB disk note). The 18 GB drive-0027 zip stays cached in runs/data/kitti/_cache/ (assert fired before unlink) so the re-run skips it — seq 00 re-extracts from cache, only seq 09 still downloads. REMAINING (next executing→executing RUN): re-run the download to GREEN, logs → runs/download_iter17.log; the new slice assertion + seq-09 raw liveness are exercised there. ~~✓ DOWNLOAD GREEN (iter 18)~~: re-ran the download (logs → runs/download_iter18.log, DOWNLOAD_EXIT=0) — Trap #10 frame-range fix PROVEN. Machine-checkable summary: seq 00 image_2=4541/4541 poses=4541/4541 calib=Y OK (drive 0027: 4544 raw → first 4541, range 0..4540); seq 09 image_2=1591/1591 poses=1591/1591 calib=Y OK (drive 0033: 1594 raw → first 1591, range 0..1590). 18.35 GB drive-0027 zip served from _cache (skip — seq 00 re-extracted from cache, only seq 09 downloaded fresh); both raw _sync zips deleted post-extract, _cache holds only the ~2 MB calib+poses zips, disk 430 GB free. On-disk: dataset/sequences/{00,09}/image_2/%06d.png + calib.txt + poses/{00,09}.txt all present in the DPVO-expected layout (Trap #1 image_2 COLOR). STEP 2 COMPLETE — Step 3 (author src/run_dpvslam.py inference wrapper, coding-only) is next.
4. 04Step 3 [DPV-SLAM++ run on seq 00 + 09] — src/run_dpvslam.py: thin wrapper around DPV-SLAM++'s official inference loop. For each sequence: load images from runs/data/kitti/dataset/sequences/<seq>/image_2/ (Trap #1 COLOR, NOT image_0 grayscale), run DPV-SLAM++ with paper-recommended config, write estimated trajectory to runs/MEASURED-001/<seq>/trajectory.tum (TUM format: timestamp tx ty tz qx qy qz qw). Also save tracking.log (per-frame FPS, residuals, loop-closure events). Single-process — NO torch.multiprocessing of CUDA tensors. Wall-clock ceiling 1h per sequence on RTX 3080 (paper extrapolation: seq 00 ~4541 frames @ 39 FPS = 2 min + LC overhead). Files: src/run_dpvslam.py, runs/MEASURED-001/{00,09}/trajectory.tum + tracking.log. ~~✓ CODING DONE (iter 19)~~: authored src/run_dpvslam.py faithful to upstream evaluate_kitti.py @ SHA 859bbbf (contract verified read-only via GitHub raw API first): kitti_image_stream reads image_2 + intrinsics P0[[0,5,2,6]]; DPVO(cfg,network,ht,wd,viz=False) on first frame; slam(t,image,intrinsics) per frame; slam.terminate() -> (poses Nx7 [tx ty tz qx qy qz qw], tstamps); TUM via evo file_interface, timestamps*stride. DPV-SLAM++ config = LOOP_CLOSURE True + CLASSIC_LOOP_CLOSURE True (both default False in config.py) + BACKEND_THRESH 32.0 (evaluate_kitti.py --backend_thresh default) + stride 2. Trap #1b handled via os.chdir(/opt/DPVO) so config/default.yaml + the classic backend's cwd-relative ORBvoc.txt both resolve (vocab_path not cfg-exposed); all data/out paths .resolve()d absolute before chdir so writes land under /workspace. tracking.log emits per-frame FPS + a machine-parseable SUMMARY json (num_frames/runtime_s/fps/has_nan/tracking_lost/start_end_dist_m/...) for Step-4's loop_closed heuristic + the No-NaN/no-tracking-lost hard-constraint checks; wrapper exits 1 on violation. Single-process CUDA preserved (only child Process is the CPU-frame image reader). ✗ RUN FAILED (iter 20): config init GREEN then died at lazy `from dpvo.dpvo import DPVO` -> `ModuleNotFoundError: No module named 'dpvo.loop_closure'` (Trap #11 — DPVO's loop_closure/ dir has no __init__.py so find_packages() dropped it from the installed wheel; upstream papers over it by running from the repo root). Reverted executing->planning. ~~✓ TRAP #11 FIX CODED (iter 21)~~: 1-file edit to src/run_dpvslam.py — `sys.path.insert(0, "/opt/DPVO")` at module top so `import dpvo` resolves the SOURCE tree (which HAS loop_closure/), reproducing upstream's run-from-repo-root model; top-level CUDA exts still resolve from site-packages. NO rebuild (image GREEN since iter 14, data staged iter 18). REMAINING (next executing->executing RUN): re-run `docker compose run --rm vision-robotics python src/run_dpvslam.py` -> runs/run_iter22.log; risks = kornia cache miss, VRAM on seq 00 (4541 frames), wall-clock ≤1h/seq; this consumes the LAST budget iter so Steps 4/5/6 are unreachable under strict one-transition-per-turn (likely terminal: budget exhausted with trajectories one good run away). ~~✓ RUN GREEN (iter 22)~~: ran `docker compose run --rm vision-robotics python src/run_dpvslam.py` -> RUN_EXIT=0; Trap #11 sys.path fix WORKED (got past the iter-20 `from dpvo.dpvo import DPVO` killer), ORBvoc.txt + LightGlue loaded (Traps #1b/#1c), BOTH trajectories produced + verified on disk: seq 00 = MEASURED-001/00/trajectory.tum (2271 poses, 309.6s, fps 8.64, has_nan=False, lost=False, 11 loop closures, start_end 9.37m); seq 09 = MEASURED-001/09/trajectory.tum (796 poses, 107.0s, fps 9.19, has_nan=False, lost=False, start_end 42.96m, LC COUNT 0). No-NaN + no-tracking-lost + wall-clock<=1h/seq hard-constraints satisfied. Finding #12 (algorithmic, not plumbing): seq 09 fired ZERO loop closures -> the seq-09 VISIBLE loop-closure hard-constraint is AT RISK; ATE (primary numeric metric) is independent + unmeasured. STEP 3 COMPLETE. This consumed the last budget iter (1->0); Steps 4/5/6 (evo eval / overlay / measure) are unreachable -> next invocation hits the budget<=0 guard -> failed (budget exhausted), a NEAR-MISS with real on-time NaN-free trajectories on disk, one evo_ape invocation short of a measured ATE.
5. 05Step 4 [evo_ape eval] — tests/eval_kitti.py: for each seq, run `evo_ape kitti --pose_relation translation_part runs/data/kitti/poses/<seq>.txt runs/MEASURED-001/<seq>/trajectory.tum --no_warnings`. Parse ATE-RMSE + RPE. Also extract loop_closed flag from tracking.log (heuristic: final pose within 5m of initial pose for seq 09). Emit per-fixture record + summary per the Shape A oracle contract. Files: tests/eval_kitti.py.
6. 06Step 5 [aerial overlay viewer] — tests/render_overlay.py: convert each seq's TUM trajectory to GPS lat/lon using KITTI's oxts GPS origin (read once from runs/data/kitti/sequences/<seq>/oxts/data/0000000000.txt). Author runs/MEASURED-001/overlay.html: self-contained Leaflet page with the Google satellite tile layer (cached at build time so it works offline), two polylines (seq 00 blue, seq 09 orange), red dot at each start/end. Visual oracle artifact. Files: tests/render_overlay.py, runs/MEASURED-001/overlay.html, runs/MEASURED-001/tiles/ (cached Karlsruhe satellite tiles).
7. 07Step 6 [measure + Shape B artifact + postmortem] — docker compose run --rm vision-robotics sh -c 'python tests/eval_kitti.py --mission M-007 --seqs 00,09 --json --save-overlay runs/MEASURED-001 > runs/MEASURED-001.json'. Validate pass_overall=true (avg ATE ≤ 27 m AND seq 09 loop_closed AND no NaN poses). Wrap as Shape B (run_id, mission, timestamp, iteration, container_invocation, primary_metric, secondary_metrics). Write POSTMORTEM.md (sandbox) + lab/missions/M-007-postmortem.md (lab mirror). Files: runs/MEASURED-001.json, POSTMORTEM.md, lab/missions/M-007-postmortem.md.

v3 metadata

1. iter 23 · executing → failed · budget-exhaustedjust now

   Entered with status=executing and iteration_budget_remaining=0. Per the mission-prompt Step-1.4 budget guard, transitioned straight to failed (budget exhausted) — no code/run transition taken (one-transition-per-turn; the run that consumed the last iter was iter 22). RESULT recorded with target_met=false and achieved_metric_value=null: the primary metric ate_rmse_kitti_avg_m was NEVER MEASURED — Steps 4/6 (evaluate_kitti.py / evo_ape) never ran, so no metrics.json or MEASURED-001.json artifact exists. This is an HONEST null, not a 0; recording any ATE number would violate the no-simulated-measurements rule. The artifact to point at from this failed state: real, NaN-free, on-time TUM trajectories for BOTH sequences on disk (seq 00 = 2271 poses/309.6s/11 LC/start_end 9.37m; seq 09 = 796 poses/107.0s/0 LC/start_end 42.96m) — the pipeline died exactly one evo_ape invocation short of a measured ATE. Central finding: this reproduces the M-006 failure mode (environment/packaging plumbing dominated the 22-iter budget), displaced from the WSL2 CUDA-IPC wall to a 7-deep single-package trap chain (#5 compose build.gpus reject, #6 wget/unzip, #7 libopencv-dev, #8 DBoW2 submodule, #9 probe false-positive, #10 KITTI frame-range, #11 loop_closure missing __init__.py), each ~2 iters — NOT an algorithmic failure of DPV-SLAM++. The single-process counter-design itself WORKED (probe gate PASS, no CUDA-IPC wall hit). Wrote the failure postmortem to lab/missions/M-007-postmortem.md and mirrored it to the sandbox POSTMORTEM.md (v3-F); appended the iter-23 CHANGELOG entry (v3-B); read the v3-B memory files before acting. Follow-up (M-008): (a) measure ATE from the already-on-disk trajectories — one cheap eval run, env+data already staged; (b) seq-09 LC=0 (Finding #12) — retune BACKEND_THRESH/stride for the visible loop-closure oracle. Process fix: front-load a build smoke + tiny dry-run into one early iter so a sequential trap chain doesn't each cost a full plan->execute->run round-trip.

   Modified: algorithm/vision-robotics/missions/M-007/CHANGELOG.md, algorithm/vision-robotics/missions/M-007/POSTMORTEM.md, lab/missions/M-007-postmortem.md

   Created: lab/missions/M-007-postmortem.md

2. iter 22 · executing → executing · execute-step3-run-greenjust now

   Ran the Step-3 SLAM RUN `docker compose run --rm vision-robotics python src/run_dpvslam.py` (logs -> runs/run_iter22.log; RUN_EXIT=0, read from the log per the Trap #6 gotcha, NOT the wrapper's outer exit). The iter-21 Trap #11 `sys.path.insert(0, "/opt/DPVO")` fix WORKED: the run got PAST the exact `from dpvo.dpvo import DPVO` import that killed iter 20 (import now resolves the source tree which physically has dpvo/loop_closure/), loaded the ORBvoc.txt vocabulary (Trap #1b - abs cwd /opt/DPVO resolves it) AND the LightGlue model from the build-warmed kornia cache (Trap #1c - no runtime network), and processed BOTH sequences to completion. Trap #11 RETIRED; the entire env+data+import chain (Traps #5-#11) is now proven end to end. BOTH trajectories produced and verified on disk: seq 00 = runs/MEASURED-001/00/trajectory.tum (2271 poses [4541 frames @ stride 2], runtime 309.6 s, fps_avg 8.64, has_nan False, tracking_lost False, 11 loop closures, start_end_dist 9.37 m); seq 09 = runs/MEASURED-001/09/trajectory.tum (796 poses [1591 @ stride 2], runtime 107.0 s, fps_avg 9.19, has_nan False, tracking_lost False, start_end_dist 42.96 m). Both tracking.log files carry a machine-parseable SUMMARY json line for Step-4. TWO hard constraints are already satisfied by the run itself: No-NaN poses + no tracking-lost (the wrapper exits 1 otherwise, so RUN_EXIT=0 proves both), and wall-clock far under the 1 h/seq ceiling (~5 min + ~2 min). HONEST FINDING #12 (algorithmic, not a plumbing trap; pinned in CLAUDE.md): seq 09 fired ZERO loop closures (`LC COUNT: 0`, start<->end 42.96 m apart) while seq 00 fired 11 - so the seq-09 VISIBLE loop-closure hard-constraint (the layperson visual oracle, the charter's whole point) is AT RISK; both backends loaded correctly so this is a genuine SLAM question (revisit not detected at BACKEND_THRESH 32.0 + stride 2), not an env failure. The numeric ATE oracle (primary metric) is independent and remains UNMEASURED. Per the run-step SUCCESS rule, status STAYS executing - the SLAM run produces trajectories, NOT the final metrics (the metric-producing eval is Step 6's evo_ape, which would set status -> measuring). No source edited this turn (run transition only). Read the v3-B memory files BEFORE running (sandbox CLAUDE.md operating manual incl. Traps #1-#11 + the M-006 counter-design; pillar charter); appended the iter-22 CHANGELOG entry and updated CLAUDE.md (Trap #11 RETIRED + Finding #12 pinned) BEFORE this write-back. PACE (the central postmortem finding, now CONFIRMED): this run consumed the LAST budget iter (iteration_budget_remaining 1 -> 0), so Steps 4/5/6 (evo eval / aerial overlay / measure + postmortem) are unreachable under strict one-transition-per-turn and the next invocation hits the budget<=0 guard -> failed (budget exhausted). But this is a NEAR-MISS, not an algorithmic failure: real, NaN-free, on-time trajectories for both sequences exist on disk - the pipeline died one evo_ape invocation short of a measured ATE. The M-006 failure mode (env/packaging plumbing dominating the budget) is reproduced here, displaced from the WSL2 CUDA-IPC wall to a 7-deep single-package trap chain (#5 compose, #6 wget, #7 opencv, #8 DBoW2, #9 probe, #10 frame-range, #11 loop_closure pkg). The trajectories are the artifact to point at from the budget-exhausted state, and seq-09 LC=0 is the substantive question a follow-up mission should open with.

   Modified: algorithm/vision-robotics/missions/M-007/CHANGELOG.md, algorithm/vision-robotics/missions/M-007/CLAUDE.md

   Created: algorithm/vision-robotics/missions/M-007/runs/run_iter22.log, algorithm/vision-robotics/missions/M-007/runs/MEASURED-001/00/trajectory.tum, algorithm/vision-robotics/missions/M-007/runs/MEASURED-001/00/tracking.log, algorithm/vision-robotics/missions/M-007/runs/MEASURED-001/09/trajectory.tum, algorithm/vision-robotics/missions/M-007/runs/MEASURED-001/09/tracking.log

3. iter 21 · planning → executing · execute-step3-trap11-syspath-fixjust now

   Applied the iter-20-diagnosed Trap #11 fix (coding only — no Docker build/run this turn, per the planning->executing rule). Single-file edit to src/run_dpvslam.py (within v3-D <=3): inserted `sys.path.insert(0, "/opt/DPVO")` at module top (after the stdlib imports, before the cv2/dpvo import chain) with a Trap #11 comment block. ROOT CAUSE recap: the installed dpvo wheel OMITS the dpvo.loop_closure subpackage — its source dir ships no __init__.py, so setup.py's packages=find_packages() stops descending and drops both dpvo.loop_closure and dpvo.loop_closure.retrieval; the iter-20 run died at the lazy `from dpvo.dpvo import DPVO` -> patchgraph.py's `from .loop_closure.optim_utils import reduce_edges` -> ModuleNotFoundError. Why this fix unblocks the run with NO rebuild: putting the SOURCE tree /opt/DPVO first on sys.path makes `import dpvo` resolve the complete source package (which physically HAS loop_closure/), reproducing upstream's run-from-repo-root model (`python evaluate_kitti.py` runs with sys.path[0]=/opt/DPVO); the top-level compiled CUDA exts (cuda_ba, cuda_corr, lietorch_backends) still resolve from site-packages regardless of which dpvo source wins. The image is already GREEN (iter 14) and KITTI seq 00/09 staged (iter 18), so this is purely a runtime import-path fix — no apt/Dockerfile/install-script change, no ~10-min rebuild; the iter-20 plan's fallback (add dpvo/loop_closure/__init__.py + rebuild) is NOT needed. Read the v3-B memory files this turn (sandbox CLAUDE.md operating manual incl. Traps #1-#11 + the M-006 counter-design; pillar charter) BEFORE editing and appended the iter-21 CHANGELOG entry (v3-B) before this write-back. 1 source file touched (src/run_dpvslam.py) — within v3-D <=3. Status -> executing. Next transition (executing->executing) is the Step-3 SLAM re-run `docker compose run --rm vision-robotics python src/run_dpvslam.py` (logs -> runs/run_iter22.log; success read from the log per the Trap #6 gotcha, not the wrapper's outer exit). PACE HONESTY (the central postmortem finding): budget is 1 after this turn. Even if the re-run produces both trajectories, it consumes the LAST budget iter, so Steps 4/5/6 (evo eval / aerial overlay / measure + postmortem) are unreachable under strict one-transition-per-turn — the most likely terminal outcome is `failed (budget exhausted)` with the import trap retired and the pipeline one good run away from trajectories. This is the M-006 failure mode (env/packaging plumbing dominated the budget) displaced from the CUDA-IPC wall to a 7-deep single-package trap chain (#5 compose, #6 wget, #7 opencv, #8 DBoW2, #9 probe, #10 frame-range, #11 loop_closure pkg) — NOT an algorithmic failure; if the re-run yields trajectories that is the artifact to point at even from a budget-exhausted state.

   Modified: algorithm/vision-robotics/missions/M-007/src/run_dpvslam.py, algorithm/vision-robotics/missions/M-007/CHANGELOG.md

4. iter 20 · executing → planning · execute-failed-step3-runjust now

   Ran the Step-3 SLAM RUN `docker compose run --rm vision-robotics python src/run_dpvslam.py` (logs -> runs/run_iter20.log; RUN_EXIT=1, read from the log per the Trap #6 gotcha, NOT the wrapper's outer exit which was 0). The FIRST real SLAM run of the baked image. It reached config init GREEN (`[run_dpvslam] seqs=['00','09'] stride=2 LOOP_CLOSURE=True CLASSIC_LOOP_CLOSURE=True BACKEND_THRESH=32.0` printed), then died at the lazy `from dpvo.dpvo import DPVO` import: dpvo/patchgraph.py:7 does `from .loop_closure.optim_utils import reduce_edges` -> `ModuleNotFoundError: No module named 'dpvo.loop_closure'` (Trap #11). ROOT CAUSE (diagnosed read-only BEFORE reverting so the next iter does not guess): the SOURCE clone /opt/DPVO/dpvo/loop_closure/ exists (long_term.py, optim_utils.py, retrieval/) but has NO `__init__.py`, while loop_closure/retrieval/ already has one. DPVO's setup.py uses `packages=find_packages()`, which STOPS descending at any dir lacking __init__.py -> so the installed wheel omits BOTH dpvo.loop_closure AND dpvo.loop_closure.retrieval (confirmed: /usr/local/lib/python3.10/dist-packages/dpvo/loop_closure is absent). Upstream never hits this: it runs `python evaluate_kitti.py` FROM the repo root, so sys.path[0]=/opt/DPVO and `import dpvo` resolves the SOURCE tree (Py3 namespace-package semantics paper over the missing init). Our wrapper runs `python src/run_dpvslam.py`, so sys.path[0]=/workspace/src -> it imports the installed (loop_closure-less) wheel and fails. The C++ extensions (cuda_ba, cuda_corr, lietorch_backends) are TOP-LEVEL modules in site-packages, importable regardless of which dpvo source wins. FIX (next planning->executing, 1-file edit to src/run_dpvslam.py, v3-D, NO REBUILD): `sys.path.insert(0, '/opt/DPVO')` at module top so `import dpvo` resolves the source tree (which has loop_closure/) -- exactly reproducing upstream's run-from-repo-root model; the top-level CUDA-ext .so's still resolve from site-packages. Fallback if path-insert surfaces an extension-load issue: add dpvo/loop_closure/__init__.py in install_dpvslam.sh before the DPVO pip install and rebuild (find_packages then includes both subpkgs) -- but that costs a ~10-min rebuild we cannot afford on budget. Per the run-step error rule, reverted executing -> planning (no source edited this turn); pinned Trap #11 in CLAUDE.md + the iter-20 CHANGELOG entry (v3-B). Read the v3-B memory files this turn (sandbox CLAUDE.md operating manual incl. Traps #1-#10 + the M-006 counter-design; pillar charter) BEFORE running. PACE HONESTY (the central postmortem finding now): budget is 2 after this turn with the Step-3 re-run + Steps 4/5/6 (evo eval / aerial overlay / measure + postmortem) ALL still ahead; under strict one-transition-per-turn this mission almost certainly CANNOT reach `done` within budget -- the 7-deep single-package trap chain (#5 compose, #6 wget, #7 opencv, #8 DBoW2, #9 probe, #10 frame-range, #11 loop_closure pkg) consumed iters 2-20. The env is fully built + KITTI data staged; the likely terminal outcome is `failed (budget exhausted)` with a near-complete pipeline -- exactly the M-006 failure mode (env plumbing dominated) this mission set out to avoid, here displaced from the CUDA-IPC wall to a deep dependency-packaging trap chain.

   Modified: algorithm/vision-robotics/missions/M-007/CHANGELOG.md, algorithm/vision-robotics/missions/M-007/CLAUDE.md

   Created: algorithm/vision-robotics/missions/M-007/runs/run_iter20.log

5. iter 19 · executing → executing · execute-step3-run-dpvslam-codingjust now

   Step-3 CODING transition (no Docker/run this turn, per one-task-per-iteration). Authored src/run_dpvslam.py — a single-process DPV-SLAM++ inference wrapper faithful to upstream princeton-vl/DPVO evaluate_kitti.py @ the pinned SHA 859bbbf, whose exact contract I verified read-only via the GitHub raw API BEFORE coding (kitti_image_stream + run + the __main__ block + dpvo.py terminate + config defaults). Contract reproduced: kitti_image_stream reads dataset/sequences/<seq>/image_2/*.png (Trap #1 COLOR, NOT image_0 grayscale) and takes intrinsics from calib['P0'][[0,5,2,6]] = (fx,fy,cx,cy); DPVO(cfg, network, ht, wd, viz=False) is constructed on the first frame; each frame fed via slam(t, image, intrinsics); slam.terminate() returns (poses Nx7 with columns [tx ty tz qx qy qz qw], tstamps) and internally runs the final global BA + appends loop-closure factors when LOOP_CLOSURE is on; the TUM file is written via evo's file_interface.write_tum_trajectory_file with timestamps*stride (upstream convention). DPV-SLAM++ config baked as the default --opts (paper Table 3, avg ATE 25.76 m on 00-10): LOOP_CLOSURE True (neural proximity) + CLASSIC_LOOP_CLOSURE True (DBoW2 long-term) — BOTH default False in dpvo/config.py so they must be set explicitly — plus BACKEND_THRESH 32.0 (evaluate_kitti.py's --backend_thresh default, NOT config.py's 64.0) and stride 2. Trap #1b handled by os.chdir('/opt/DPVO') so the two cwd-relative upstream defaults resolve: config/default.yaml AND the classic backend's RetrievalDBOW(vocab_path='ORBvoc.txt') (the vocab path is not cfg-exposed, so chdir is the clean fix rather than a per-arg path); every dataset + output path is .resolve()d to absolute BEFORE the chdir so writes still land under /workspace. Outputs per seq: runs/MEASURED-001/<seq>/trajectory.tum + tracking.log; tracking.log carries per-frame FPS lines (every 50 frames) and a machine-parseable final 'SUMMARY {json}' line (num_frames, num_poses, runtime_s, fps_avg/min, has_nan, tracking_lost, start_xyz, end_xyz, start_end_dist_m, loop_closure_enabled) — this feeds Step-4's loop_closed heuristic (start_end_dist_m <= threshold for the loop seqs) and the No-NaN-poses / no-tracking-lost hard-constraint checks (the wrapper returns exit 1 if any sequence violates them). Single-process CUDA invariant preserved: the only child Process is the image reader putting CPU numpy frames on a bounded Queue; .cuda() happens only in the main process after queue.get() — no live CUDA tensor crosses a boundary (the M-006 counter-design; already build-gated by probe_no_cuda_ipc.py). Read the v3-B memory files this turn (sandbox CLAUDE.md operating manual incl. Traps #1-#10 + the M-006 counter-design, pillar charter) BEFORE coding, and appended the iter-19 CHANGELOG entry before this write-back. 1 source file touched (src/run_dpvslam.py) — within v3-D <=3. Status STAYS executing (Step-3 RUN + Steps 4/5/6 remain). Honest carry-forward (the gating unknown): the wrapper has NOT run — the next executing->executing transition runs `docker compose run --rm vision-robotics python src/run_dpvslam.py`, the FIRST time the baked image runs SLAM on real frames (sm_86 CUDA kernels + the LOOP_CLOSURE+CLASSIC backends loading ORBvoc.txt + the warmed kornia DISK/LightGlue cache). Risks to watch on that run: a cwd/ORBvoc miss, a kornia cache miss (would need network), VRAM headroom on the 4541-frame seq 00, and the wall-clock <=1h/seq ceiling; a failure reverts executing->planning. Pace honesty for the postmortem: 3 budget iters remain after this turn with Step-3-run + Steps 4/5/6 (evo eval / aerial overlay / measure) all ahead — the mission's tightest schedule risk; seq 00 (4541 frames) + seq 09 + both loop-closure backends + the evo measure may not all land in 3 iters.

   Modified: algorithm/vision-robotics/missions/M-007/src/run_dpvslam.py, algorithm/vision-robotics/missions/M-007/CHANGELOG.md

6. iter 18 · executing → executing · execute-step2-download-greenjust now

   Ran the Step-2 download RUN `docker compose run --rm vision-robotics python src/setup/download_kitti.py` (logs -> runs/download_iter18.log; DOWNLOAD_EXIT=0, read from the log per the Trap #6 gotcha, not the wrapper's outer exit). The iter-17 Trap #10 frame-range fix WORKED and Step 2 is now COMPLETE. The script's deterministic machine-checkable verification PASSED for both sequences: `seq 00: image_2=4541/4541 poses=4541/4541 calib=Y OK` (raw drive 2011_10_03_drive_0027 extracted 4544 color frames -> sliced to the first 4541, range 0..4540) and `seq 09: image_2=1591/1591 poses=1591/1591 calib=Y OK` (raw drive 2011_09_30_drive_0033 extracted 1594 frames -> sliced to the first 1591, range 0..1590), ending `DONE — KITTI seq 00,09 ready`. The new `all_frames[start:end+1]` slice + `>= end+1` assertion are now exercised and PROVEN against real data; the iter-16 full-drive-equality failure (4544 != 4541) is decisively retired. Cheap re-run exactly as predicted: the 18.35 GB 2011_10_03_drive_0027_sync.zip was served from runs/data/kitti/_cache/ (`cached … 18350 MB — skip`), so seq 00 re-extracted from cache and only seq 09's drive 0033 (~smaller) downloaded fresh over S3 (s3.eu-central-1.amazonaws.com/avg-kitti — seq-09 raw liveness confirmed). Both raw _sync zips were deleted post-extract (disk budget): _cache/ now holds only the ~2 MB official calib+poses zips and disk is back to 430 GB free on D: (the transient 18 GB peak was a cache hit, never re-fetched). On-disk confirmation: dataset/sequences/{00,09}/image_2/%06d.png populated (seq 00 spans 000000.png..004540.png), calib.txt present per sequence, poses/{00,09}.txt present (official odometry GT in the cam0 frame) — the layout DPVO's evaluate_kitti.py expects (Trap #1: image_2 COLOR, not image_0 grayscale). No new trap discovered; Trap #10 is now CONFIRMED-FIXED. Log-naming note: the iter-17 plan referenced download_iter17.log, but the run executes at iter 18 so the log is named by the producing iteration (download_iter18.log). Per the run-step success rule, status STAYS executing (the download is a Step-2 setup gate, not the final eval). No source edited this turn (run transition only); read the v3-B memory files (sandbox CLAUDE.md operating manual incl. Traps #1-#10 + the M-006 counter-design, pillar charter) BEFORE running and appended the iter-18 CHANGELOG entry (v3-B) before this write-back. Next transition (executing->executing) begins Step 3: author src/run_dpvslam.py — a single-process wrapper around DPV-SLAM++'s inference loop that reads dataset/sequences/<seq>/image_2/, runs with paper config (LOOP_CLOSURE True for seq 09, ORBvoc.txt at the abs path /opt/DPVO/ORBvoc.txt per Trap #1b), and writes runs/MEASURED-001/<seq>/trajectory.tum + tracking.log. Pace honesty for the postmortem: 4 budget iters remain after this turn with Steps 3/4/5/6 (DPV-SLAM++ run / evo eval / aerial overlay / measure) all ahead — the mission's tightest schedule risk; the gating unknown is now the Step-3 inference run (first time the baked image runs SLAM on real frames: CUDA sm_86 kernels + loop-closure backend).

   Modified: algorithm/vision-robotics/missions/M-007/CHANGELOG.md

   Created: algorithm/vision-robotics/missions/M-007/runs/download_iter18.log

7. iter 17 · planning → executing · execute-step2-trap10-frame-range-fixjust now

   Applied the iter-16-diagnosed Trap #10 fix (coding only — no Docker build/run this turn, per the planning->executing rule). Single source-file edit to src/setup/download_kitti.py (within v3-D <=3), three coordinated changes: (1) SEQ_TO_RAW now carries a per-seq frame RANGE — 00: start 0 end 4540 (4541 frames), 09: start 0 end 1590 (1591) — encoding the official KITTI odometry->raw mapping (devkit train_mapping.txt), which is a frame-RANGE SUBSET of the source raw drive, NOT the full drive (raw drive 0027 has 4544 frames; odometry seq 00 is the first 4541, last 3 trimmed). (2) fetch_sequence_images now slices `frames = all_frames[start:end+1]` and asserts `len(all_frames) >= end+1` (the raw drive must have AT LEAST the odometry count) instead of the old full-drive equality assert that fired at iter 16 (`raw drive gave 4544 frames, expected 4541`). (3) docstring corrected — the mapping line now reads 'frame-RANGE subset' not 'exact and full-drive', and the wrong '~1.5-4.7 GB each' raw-zip estimate is replaced with the real 18.35 GB drive-0027 disk note + the honest disk-budget caveat (transiently exceeds host_requirements.disk_gb:12; 435 GB free on D:, zip deleted post-extract -> ~3 GB steady state; no smaller official source exists since the odometry color zip is 65 GB). Why this unblocks: iter 16 already proved the S3 hosting LIVE, the official poses+calib correct (00=4541, 09=1591 GT lines), and the raw extraction working (4544 frames out of drive 0027) — only the slice arithmetic was wrong. Both sequences start at raw frame 0, so the fix is exactly 'keep the first `expected` frames in sorted order'; it stays deterministic + idempotent (the complete-sequence skip still holds since len(frames) after slice == expected). Data-cache win still stands: the 18.35 GB 2011_10_03_drive_0027_sync.zip remains in runs/data/kitti/_cache/ from iter 16 (the ValueError fired BEFORE the post-extract unlink), so _download skips it on the re-run — seq 00 re-extracts from cache instantly; only seq 09's drive 0033 still downloads. Read the v3-B memory files this turn (sandbox CLAUDE.md operating manual incl. Traps #1-#10 + the M-006 counter-design; pillar charter) BEFORE editing, and appended the iter-17 CHANGELOG entry before this write-back. Status stays executing (Step 2's download RUN + Steps 3-6 remain). Honest carry-forward (the remaining unknown): the script has NOT re-run — the new slice assertion + seq-09 raw-drive liveness are only exercised when it RUNS next (executing->executing: `docker compose run --rm vision-robotics python src/setup/download_kitti.py` -> runs/download_iter17.log); a seq-09 404 or count mismatch surfaces there and reverts executing->planning. Pace honesty for the postmortem: 5 budget iters remain after this turn with Step-2-run (re-run) + Steps 3/4/5/6 (DPV-SLAM++ run / evo eval / aerial overlay / measure) all ahead — the mission's tightest schedule risk; the seq-00 18 GB download (~45 min wall) is sunk/cached so the re-run is cheap, and the gating unknown is the Step-3 inference run.

   Modified: algorithm/vision-robotics/missions/M-007/src/setup/download_kitti.py, algorithm/vision-robotics/missions/M-007/CHANGELOG.md

8. iter 16 · executing → planning · execute-failed-step2-downloadjust now

   Ran the Step-2 download RUN `docker compose run --rm vision-robotics python src/setup/download_kitti.py` (logs -> runs/download_iter16.log; DOWNLOAD_EXIT=1, read from the log). The KITTI S3 hosting is LIVE and the assembler got far: the official data_odometry_poses.zip + data_odometry_calib.zip fetched + extracted (44 calib members; verified poses/00.txt=4541 lines, poses/09.txt=1591 lines), and the raw 2011_10_03_drive_0027_sync.zip downloaded IN FULL (18,350 MB) and extracted 4544 color frames. It then FAILED at the frame-count assertion (download_kitti.py L207): `seq 00: raw drive gave 4544 frames, expected 4541`. ROOT CAUSE (Trap #10): KITTI odometry sequences are a frame-RANGE SUBSET of their source raw drive, NOT the full drive as SEQ_TO_RAW assumed. The official odometry->raw mapping (devkit train_mapping.txt) is a range: seq 00 = drive 0027 frames 000000..004540 (first 4541 of 4544 — last 3 trimmed); seq 09 = drive 0033 frames 000000..001590 (first 1591). Both start at frame 0. Secondary finding: the raw _sync zip is 18.35 GB (monolithic — 4 cameras + velodyne + oxts), NOT the ~4.7 GB the script comment estimated, so it transiently exceeds host_requirements.disk_gb:12 — but this is NOT a hard failure (435 GB free on D:, the zip is deleted post-extract leaving ~3 GB steady state) and no smaller OFFICIAL source exists (the odometry color zip is 65 GB, strictly worse), so the raw-drive assembler design stands; only the slice + the disk-size note need fixing. Per the run-step error rule, reverted executing -> planning (no code edited this turn) and diagnosed the fix read-only so the next iteration does not guess. FIX (next planning->executing, 1-file edit to src/setup/download_kitti.py, v3-D): add per-seq start/end to SEQ_TO_RAW (00: 0..4540; 09: 0..1590), extract sorted(frames)[start:end+1], and assert len(all_frames) >= end+1 (raw drive must have AT LEAST the odometry count) instead of asserting full-drive equality; since both start at 0 this is 'take the first `expected` frames'. DATA-CACHE WIN: the 18.35 GB drive-0027 zip is STILL in runs/data/kitti/_cache/ (the ValueError fires BEFORE the post-extract unlink), so _download skips it on re-run — seq 00 re-extracts from cache instantly and only seq 09's ~7 GB drive 0033 still downloads, so the ~45-min seq-00 fetch is sunk/cached and the re-run is cheap. Pinned Trap #10 in CLAUDE.md + the iter-16 CHANGELOG entry (v3-B). Pace honesty for the postmortem: 6 budget iters remain after this turn with Step-2-run (re-run) + Steps 3/4/5/6 (DPV-SLAM++ run / evo eval / aerial overlay / measure) all ahead — the tightest schedule risk of the mission; the gating unknown now shifts from data download to the Step-3 inference run.

   Modified: algorithm/vision-robotics/missions/M-007/CHANGELOG.md, algorithm/vision-robotics/missions/M-007/CLAUDE.md

   Created: algorithm/vision-robotics/missions/M-007/runs/download_iter16.log

9. iter 15 · executing → executing · execute-step2-download-kitti-coding2h ago

   Step-2 CODING transition (no Docker/run this turn, per one-task-per-iteration). Authored src/setup/download_kitti.py — fetches KITTI Odometry seq 00 + 09 COLOR (image_2, per Trap #1, CORRECTING the original code_plan's grayscale image_0) plus the official GT poses + calib into runs/data/kitti/dataset/ in the DPVO-expected layout (sequences/<seq>/image_2/%06d.png + poses/<seq>.txt + sequences/<seq>/calib.txt). KEY DESIGN CALL surfaced for the postmortem: the official Odometry COLOR distribution is a single monolithic data_odometry_color.zip (~65 GB, all 22 sequences) — it cannot be sliced to two sequences and blows host_requirements.disk_gb:12 many times over (the grayscale zip ~22 GB is the same problem). The only budget-feasible OFFICIAL path is to assemble the two sequences from the KITTI raw per-drive _sync archives (individually downloadable ~1.5-4.7 GB each, deleted after extracting ONLY image_02/data), pairing them with the tiny official Odometry poses+calib zips (~5 MB). Published Odometry->raw full-drive mapping encoded as SEQ_TO_RAW: seq 00 = 2011_10_03_drive_0027 (4541 frames), seq 09 = 2011_09_30_drive_0033 (1591). Consistency argued in-file: the raw _sync image_02 IS the same rectified left-color frame the Odometry image_2 is derived from, so pairing it with the official Odometry P0..P3 calib + cam0-frame GT poses is consistent for the evo Sim3 ATE oracle (align=True, correct_scale=True); honest caveat noted (Odometry applies a uniform crop, but drives 0027/0033 use the native rectified size so no crop mismatch for 00/09). Engineering: stdlib-only (urllib + zipfile — no wget/unzip dependency, runs identically on host or in-container), atomic .part downloads, idempotent (skips complete sequences, rebuilds partial ones), Trap #2 guard (asserts GT pose lines are 12-col 3x4), raw zip deleted post-extract to respect the 12 GB budget, and a final machine-checkable verification (image_2 count == poses count == expected AND calib present, non-zero exit on mismatch). Second file: added a sandbox .gitignore (runs/data/, runs/MEASURED-*/tiles/, __pycache__) — `git check-ignore` confirmed runs/data was NOT previously ignored and no sandbox .gitignore existed, yet the pillar manual requires runs/data gitignored + sync-excluded and the download writes ~2-3 GB next transition; this prevents an accidental multi-GB commit. 2 source files touched (download_kitti.py + .gitignore) — within v3-D <=3. Read the v3-B memory files this turn (sandbox CLAUDE.md operating manual incl. Traps #1-#9 + the M-006 counter-design, pillar charter) and appended the iter-15 CHANGELOG entry before this write-back. Status STAYS executing (Step 2's download RUN + Steps 3-6 remain). Honest carry-forward (the remaining unknown): the script has NOT run — raw S3 URL liveness (s3.eu-central-1.amazonaws.com/avg-kitti/raw_data/...) and the full-drive frame-count assertions are only exercised when it RUNS in the next executing->executing transition (`docker compose run --rm vision-robotics python src/setup/download_kitti.py`); a 404 or count mismatch surfaces there and reverts executing->planning. Pace honesty: 7 budget iters remain after this turn with Steps 2-run/3/4/5/6 (download/run/eval/overlay/measure) all ahead — the tightest schedule risk of the mission, to surface in the postmortem.

   Modified: algorithm/vision-robotics/missions/M-007/src/setup/download_kitti.py, algorithm/vision-robotics/missions/M-007/.gitignore, algorithm/vision-robotics/missions/M-007/CHANGELOG.md

10. iter 14 · executing → executing · execute-step1-build-green2h ago

    Ran the Step-1 gate `docker compose build vision-robotics` (logs -> runs/build_iter10.log; BUILD_EXIT=0, confirmed by READING the log per the Trap #6 gotcha, not just the wrapper exit). The iter-13 Trap #9 probe-walk fix WORKED and the image went fully GREEN for the first time. The deterministic probe BUILD GATE (v3-C oracle) PASSED: build_iter10.log L44/87/88/104 show `single_process_cuda_safe: true`, `unaudited_call_sites: []`, `cpu_fence_ok: true`, and `[probe_no_cuda_ipc] PASS - all mp call sites audited; CUDA stays in-process.` - the iter-12 false-positive over-collection is gone AND the real single-process / no-CUDA-IPC invariant holds (the M-006 trap is decisively avoided, statically proven). Image exported + named `docker.io/library/blueberry-m007-dpvslam:latest` (manifest sha256:4a6590e0..., L110-117) and Compose reported `vision-robotics Built`. As predicted, the upstream layers (torch 2.3.1+cu121, the dpvo nvcc sm_86 CUDA wheel, the dpretrieval DBoW C++/cmake wheel, the DBoW2 build+install) all stayed CACHED because the probe is a late COPY/RUN layer - only the probe RUN + the image export/unpack (~285s) executed, so the effective cost was ~1 iter. STEP 1 IS RETIRED: the entire env chain is proven end-to-end and Traps #5 (compose build.gpus reject), #6 (wget/unzip), #7 (libopencv-dev), #8 (DBoW2 submodule build), #9 (probe walk false positive) are ALL retired. Per the run-step success rule, status STAYS executing (this build was a Step-1 setup gate, not the final eval). No source was edited this turn (run/build transition only); appended the iter-14 CHANGELOG entry (v3-B) before this write-back. Next transition (executing->executing) begins Step 2: author src/setup/download_kitti.py to fetch the KITTI Odometry COLOR set (image_2 stream, per Trap #1 - NOT grayscale image_0) for sequences 00 + 09 plus the ground-truth pose files into runs/data/kitti/ (gitignored + sync-excluded). Pace honesty for the postmortem: 8 budget iters remain after this turn; the build phase consumed iters 2-14 (Traps #5-#9, ~2 iters each), so Steps 2-6 (KITTI download/run/eval/overlay/measure) must land in 8 iters - a real schedule risk to surface.

    Modified: algorithm/vision-robotics/missions/M-007/CHANGELOG.md

    Created: algorithm/vision-robotics/missions/M-007/runs/build_iter10.log

11. iter 13 · planning → executing · execute-step1-trap9-probe-walk-fix8h ago

    Applied the iter-12-planned Trap #9 fix (coding only — no Docker build/run this turn, per the planning->executing rule). Single-file edit to src/setup/probe_no_cuda_ipc.py (within v3-D <=3): (a) extended iter_py_files's skip set to also exclude `build/` (the setuptools artifact tree — literal COPIES of already-audited dpvo/*.py, never imported at runtime) and `Pangolin/` (GUI/build tooling for the DPViewer we deliberately skip — no X display, overlay.html renders offline), joining the existing pybind11/ + DPViewer/ + DPRetrieval/ skips; (b) added the three sibling evaluate_{euroc,icl_nuim,tum}.py to the AUDITED allowlist, each with the verified CPU-Queue rationale (Process(target=*_image_stream) reader .cpu().numpy()s frames over a Queue; .cuda() only in the MAIN process after queue.get() — structurally identical to the audited evaluate_kitti.py at SHA 859bbbf, confirmed read-only in iter 12). The regression tripwire stays HONEST: a genuinely NEW mp call site in a runtime file, or a dropped long_term.py .cpu() fence, still fails the build. This is legitimately in-bounds and NOT the CLAUDE.md 'STOP — do NOT fix' case: that rule targets a real shared-CUDA-tensor discovery (cpu_fence_ok:false), but the gate's REAL invariant already PASSED at iter 12 (cpu_fence_ok:true, missing_cpu_fences:[]) — only the secondary completeness tripwire's directory walk was over-collecting 6 non-runtime files. Why this should carry the build to GREEN: iter 12 already proved every upstream stage GREEN through `Successfully built dpvo` (nvcc sm_86 CUDA wheel) AND `Successfully built dpretrieval` (DBoW C++/cmake wheel); the probe gate was the only remaining failure and this fix removes its false positive. Read v3-B memory files this turn (sandbox CLAUDE.md operating manual incl. Traps #5-#9, pillar charter) and appended the iter-13 CHANGELOG entry before this write-back. Status -> executing. Next transition (executing->executing) runs `docker compose build vision-robotics` to GREEN, logs -> runs/build_iter10.log; per the Trap #6 gotcha, success is read from the log (BUILD_EXIT= / 'failed to solve:'), NOT the wrapper's outer exit code. The probe is a LATE Dockerfile COPY/RUN layer, so torch + dpvo + dpretrieval all stay CACHED — the re-run reaches the probe in well under a minute (cheap, ~1 effective iter). A GREEN build retires Step 1 (the entire env chain — Traps #5-#9 all retired) and unblocks Step 2 (KITTI color odometry download). Pace honesty for the postmortem: 9 budget iters remain after this turn with Steps 2-6 (KITTI download/run/eval/overlay/measure) still ahead — the build phase consumed iters 2-13, so Steps 2-6 must land in 9 iters; a real schedule risk to surface.

    Modified: algorithm/vision-robotics/missions/M-007/src/setup/probe_no_cuda_ipc.py, algorithm/vision-robotics/missions/M-007/CHANGELOG.md

12. iter 12 · executing → planning · execute-failed-step1-build8h ago

    Ran the Step-1 gate `docker compose build vision-robotics` (logs -> runs/build_iter9.log; BUILD_EXIT=1 read from the log per the Trap #6 gotcha, NOT the wrapper's outer exit which was 0). MAJOR progress: the iter-11 Trap #8 DBoW2 build+install fix WORKED and so did Trap #7 — BOTH previously-unproven upstream stages are now GREEN: `Successfully built dpvo` (build_iter9.log L401, the nvcc sm_86 CUDA-extension wheel) AND `Successfully built dpretrieval` (L461, the DBoW loop-closure C++/cmake wheel now that OpenCV + DBoW2 are both satisfied). The entire upstream C++/CUDA build chain is retired. The build then reached the FINAL stage for the first time ever — our own `probe_no_cuda_ipc.py` BUILD GATE — and exited 1. CRUCIAL READING: the gate's REAL invariant PASSED — `cpu_fence_ok: true`, `missing_cpu_fences: []` (the long_term.py loop-closure `.cpu()` fences are intact, so NO live CUDA tensor crosses any process boundary). What failed is the probe's SECONDARY completeness tripwire ('fail on any mp call site not on the AUDITED allowlist'), which over-collected 6 NON-RUNTIME files: (1) `build/lib.linux-x86_64-cpython-310/dpvo/{dpvo,stream}.py` — setuptools BUILD-ARTIFACT copies of the already-audited `dpvo/dpvo.py` + `dpvo/stream.py`; (2) `Pangolin/scripts/vcpkg/scripts/generatePortVersionsDb.py` — Pangolin GUI/build tooling we deliberately never build or import (DPViewer is skipped); (3) `evaluate_euroc.py`, `evaluate_icl_nuim.py`, `evaluate_tum.py` — sibling eval scripts for datasets we DO NOT run (M-007 runs KITTI via the audited `evaluate_kitti.py`). Verified read-only against the pinned SHA 859bbbf BEFORE reverting so the next iteration does not guess: fetched all three sibling eval scripts; they are STRUCTURALLY IDENTICAL to the audited evaluate_kitti.py — `Process(target=*_image_stream, args=(queue,...))` reader, the stream fn `.cpu().numpy()`s frames and `queue.put`s them, and the MAIN process does `queue.get()` then `.cuda()` (euroc/icl L41-42, kitti L83, tum L76). No live CUDA tensor crosses the boundary in any of them. CONCLUSION: this is a FALSE POSITIVE in OUR OWN defensive gate's directory walk, NOT a genuine upstream single-process violation — the CLAUDE.md 'STOP, do NOT try to fix' rule targets a real shared-CUDA-tensor discovery (`cpu_fence_ok: false`), which did not occur; fixing the probe's walk is in-bounds. Per the run-step error rule, reverted executing -> planning (no code edited this turn) and pinned Trap #9 in CLAUDE.md + the iter-12 CHANGELOG entry (v3-B). Next iteration (planning -> executing) makes a 1-file edit to src/setup/probe_no_cuda_ipc.py (within v3-D <=3): (a) extend `iter_py_files` to also skip `build/` (setuptools artifact tree) and `Pangolin/` (GUI/build dep — consistent with the existing DPViewer/DPRetrieval/pybind11 skips); (b) ADD the three sibling evaluate_*.py to the AUDITED allowlist with the verified CPU-Queue rationale, keeping the regression tripwire honest (a genuinely NEW mp call site in a runtime file still fails the build). Then re-run -> runs/build_iter10.log. Build-cache note: the probe is a LATE COPY/RUN layer, so torch + dpvo + dpretrieval all stay CACHED — the re-run reaches the probe in seconds, so the effective cost is ~1 iter (not the ~2-iter tax of the earlier upstream blockers). Pace honesty for the postmortem: the upstream build chain is now fully GREEN and this is the LAST build blocker (our own gate); 10 budget iters remain with Steps 2-6 (KITTI download/run/eval/overlay/measure) ahead.

    Modified: algorithm/vision-robotics/missions/M-007/CHANGELOG.md, algorithm/vision-robotics/missions/M-007/CLAUDE.md

    Created: algorithm/vision-robotics/missions/M-007/runs/build_iter9.log

13. iter 11 · planning → executing · execute-step1-trap8-dbow2-fix3h ago

    Applied the iter-10-planned Trap #8 fix (coding only — no Docker build/run this turn, per the planning->executing rule). Single-file edit to src/setup/install_dpvslam.sh: inserted a new step 7b that builds + `make install`s the bundled DBoW2 submodule BETWEEN the DPVO wheel (step 7) and the DPRetrieval wheel (step 8): `cd ${DPVO_DIR}/DBoW2 && mkdir -p build && cd build && cmake .. -DCMAKE_BUILD_TYPE=Release && make -j$(nproc) && make install && ldconfig`. This is the minimal unblock for the iter-8/iter-10 failure: DPVO bundles DBoW2 as a top-level git submodule that `clone --recursive` FETCHES but nothing BUILDS, so DPRetrieval's CMakeLists.txt:6 `find_package(DBoW2 REQUIRED)` could not find DBoW2Config.cmake. No Dockerfile/apt change is needed (verified read-only in iter 10): the lahavlipson/DBoW2 fork depends ONLY on OpenCV (already present via libopencv-dev from the Trap #7 fix; DLib is vendored in its src/), and `make install` writes DBoW2Config.cmake to /usr/local/lib/cmake/DBoW2/ — exactly the path DPRetrieval's find_package(DBoW2) searches. Why this should carry the build through: the iter-8 build already proved every stage GREEN through the expensive dpvo nvcc sm_86 wheel AND past the DPRetrieval OpenCV check (build_iter8.log L2603 `Found OpenCV 4.5.4`); only the second find_package(DBoW2) failed. Honest carry-forward (the remaining unknowns): TWO stages stay UNPROVEN until the next build — (1) the dpretrieval C++/cmake wheel now that BOTH OpenCV and DBoW2 are satisfied, and (2) the probe_no_cuda_ipc.py BUILD GATE, which has never executed because every prior build aborted before its COPY/RUN. Read v3-B memory files this turn (sandbox CLAUDE.md operating manual incl. Traps #5/#6/#7/#8, pillar charter) and appended the iter-11 CHANGELOG entry before this write-back. Status -> executing. Next transition (executing->executing) runs `docker compose build vision-robotics` to GREEN, logs -> runs/build_iter9.log; per the Trap #6 gotcha, success is read from the log (`BUILD_EXIT=` / `failed to solve:`), not the wrapper's outer exit code. Adding the DBoW2 step edits install_dpvslam.sh, which invalidates from the install layer, so torch re-downloads + dpvo re-compiles (~7-8 min) before reaching DBoW2 -> DPRetrieval -> the probe gate. Pace honesty for the postmortem: this is the 4th sequential one-package build trap fixed (#5 compose, #6 wget, #7 opencv, #8 DBoW2); 11 budget iters remain with Steps 2-6 (KITTI download/run/eval/overlay/measure) still ahead — a real schedule risk to surface.

    Modified: algorithm/vision-robotics/missions/M-007/src/setup/install_dpvslam.sh, algorithm/vision-robotics/missions/M-007/CHANGELOG.md

14. iter 10 · executing → planning · execute-failed-step1-build3h ago

    Ran the Step-1 gate `docker compose build vision-robotics` (logs -> runs/build_iter8.log; BUILD_EXIT=1, read from the log per the Trap #6 gotcha, NOT the wrapper's outer exit which was 0). The iter-9 Trap #7 `libopencv-dev` fix WORKED: the dpretrieval cmake configure now gets PAST OpenCV — build_iter8.log L2603 `-- Found OpenCV: /usr (found version "4.5.4")`. The build then died ONE find_package further, at `DPRetrieval/CMakeLists.txt:6`: `find_package(DBoW2 REQUIRED)` -> `Could not find a package configuration file provided by "DBoW2" ... DBoW2Config.cmake`. Root cause (Trap #8): DPVO bundles DBoW2 as a TOP-LEVEL git submodule (`.gitmodules` path `DBoW2`, url lahavlipson/DBoW2.git); `git clone --recursive` in install_dpvslam.sh step 5 fetches its SOURCE, but nothing BUILDS or INSTALLS it, so the `DBoW2Config.cmake` that DPRetrieval's find_package needs never exists. install_dpvslam.sh jumps straight from the DPVO wheel (step 7) to `pip install ./DPRetrieval` (step 8) with no DBoW2 build between. Diagnosed the exact fix read-only before reverting (so the next iteration doesn't guess): fetched `lahavlipson/DBoW2/CMakeLists.txt` — it depends ONLY on OpenCV (already satisfied by libopencv-dev; DLib is vendored in its src/, so NO new apt dep) and its install rules write `DBoW2Config.cmake` to `${CMAKE_INSTALL_PREFIX}/lib/cmake/DBoW2/` (default prefix /usr/local), precisely where DPRetrieval's find_package(DBoW2) searches. The probe_no_cuda_ipc.py BUILD GATE STILL has not run (install layer aborts before its COPY/RUN) and remains the only unexercised stage after DPRetrieval. Per the run-step error rule, reverted executing -> planning (no fix coded this turn) and pinned Trap #8 in CLAUDE.md + the iter-10 CHANGELOG entry (v3-B). Next iteration (planning -> executing) inserts a DBoW2 build+install step in install_dpvslam.sh between current steps 7 and 8 (`cd $DPVO_DIR/DBoW2 && mkdir -p build && cd build && cmake .. -DCMAKE_BUILD_TYPE=Release && make -j$(nproc) && make install && ldconfig`) — a 1-file edit (v3-D) with no Dockerfile/apt change — and re-runs the build -> runs/build_iter9.log. Build-cache note: editing install_dpvslam.sh invalidates from the install layer, so torch re-downloads + dpvo re-compiles (~7-8 min) before reaching DBoW2 -> DPRetrieval -> the probe gate. Pace honesty for the postmortem: this is the 4th sequential one-package build trap (#5 compose, #6 wget, #7 opencv, #8 DBoW2), each costing ~2 iters; 12 budget iters remain with Steps 2-6 (KITTI download/run/eval/overlay/measure) still ahead — a real schedule risk to surface.

    Modified: algorithm/vision-robotics/missions/M-007/CHANGELOG.md, algorithm/vision-robotics/missions/M-007/CLAUDE.md

    Created: algorithm/vision-robotics/missions/M-007/runs/build_iter8.log

15. iter 9 · planning → executing · execute-step1-trap7-libopencv-fix3h ago

    Applied the iter-8-planned Trap #7 fix (coding only — no Docker build/run this turn, per the planning->executing rule). Single-file edit to the Dockerfile: added `libopencv-dev` to the apt-get --no-install-recommends list (right after libeigen3-dev) with a Trap #7 comment block, because the DBoW loop-closure submodule DPRetrieval is a native C++/cmake build whose CMakeLists.txt:3 does find_package(OpenCV) and needs the SYSTEM OpenCV cmake config (OpenCVConfig.cmake). The opencv-python-headless wheel install_dpvslam.sh installs does NOT ship that config, so the iter-8 build died at the dpretrieval wheel with `CMake Error ... Could not find a package configuration file provided by "OpenCV"` (libopencv-dev count in build_iter7.log = 0, confirming it was never present). This is the minimal unblock: the iter-8 build already proved every prior stage GREEN through the EXPENSIVE part — base pull, apt layer, torch 2.3.1+cu121 + torch_scatter + python deps, DPVO clone @ 859bbbf, and DECISIVELY the dpvo nvcc sm_86 CUDA-extension wheel (~231s, build_iter7.log 990-996) — so only the dpretrieval wheel onward re-runs after OpenCV is added. Honest carry-forward (the remaining unknowns): the dpretrieval C++/cmake wheel (now that system OpenCV is present) and the probe_no_cuda_ipc.py BUILD GATE (which has never executed — every prior build aborted before its COPY/RUN) are both still UNPROVEN; they are the only stages left before the image reaches GREEN. Read v3-B memory files this turn (sandbox CLAUDE.md operating manual incl. Traps #5/#6/#7, pillar charter) and appended the iter-9 CHANGELOG entry before this write-back. Status -> executing. Next transition (executing->executing) runs `docker compose build vision-robotics` to GREEN, logs -> runs/build_iter8.log; per the Trap #6 gotcha, success is read from the log (`BUILD_EXIT=` / `failed to solve:`), not the wrapper's outer exit code. Adding to the apt layer invalidates everything after it, so torch re-downloads + dpvo re-compiles (~7-8 min) before reaching the new dpretrieval + probe stages.

    Modified: algorithm/vision-robotics/missions/M-007/Dockerfile, algorithm/vision-robotics/missions/M-007/CHANGELOG.md

16. iter 8 · executing → planning · execute-failed-step1-build3h ago

    Ran the Step-1 gate `docker compose build vision-robotics` (logs -> runs/build_iter7.log; BUILD_EXIT=1 read from the log per the Trap #6 gotcha, NOT the wrapper's outer exit). The iter-7 Trap #6 wget/unzip fix WORKED and the build advanced dramatically further than iter 6: base pull, apt layer (now incl. wget+unzip), torch 2.3.1+cu121 + torch_scatter + all python deps (evo 1.36.5, kornia 0.8.2, opencv-python-headless 4.11, numpy 1.26.4, ...) GREEN, DPVO cloned @ 859bbbf, and DECISIVELY the `dpvo` wheel BUILT GREEN (log lines 990-996, ~231 s of nvcc sm_86 compile of DPVO + lietorch CUDA extensions). This RETIRES the single biggest remaining unknown flagged since iter 3: the sm_86 cross-compile for the RTX 3080 actually works with TORCH_CUDA_ARCH_LIST=8.6 and no build-time GPU. The build then died at the very next install step — building the `dpretrieval` wheel (the DBoW2 loop-closure retrieval submodule, a native C++/cmake build): `DPRetrieval/CMakeLists.txt:3 find_package(OpenCV)` failed with `CMake Error ... Could not find a package configuration file provided by "OpenCV" ... OpenCVConfig.cmake`. Root cause (Trap #7): the image has only the `opencv-python-headless` PYTHON wheel, which does not ship the C++ cmake config; DPRetrieval needs the SYSTEM `libopencv-dev` package (grep count of `libopencv-dev` in the build log = 0, confirming it was never installed). The probe_no_cuda_ipc.py BUILD GATE still has not run (the install layer aborts before the probe COPY/RUN) and remains the only unexercised stage. Per the run-step error rule, reverted executing -> planning (no fix coded this turn) and pinned Trap #7 in CLAUDE.md + the iter-8 CHANGELOG entry (v3-B). Next iteration (planning -> executing) adds `libopencv-dev` to the Dockerfile apt-get list (1-file edit, v3-D) and re-runs the build -> runs/build_iter8.log; the apt-layer edit re-downloads torch + re-compiles dpvo (~7-8 min) before reaching DPRetrieval again, which should then build and carry the image GREEN through the probe gate. With dpvo's CUDA compile now proven, this is the last expected build blocker before Step 2 (KITTI download).

    Modified: algorithm/vision-robotics/missions/M-007/CHANGELOG.md, algorithm/vision-robotics/missions/M-007/CLAUDE.md

    Created: algorithm/vision-robotics/missions/M-007/runs/build_iter7.log

17. iter 7 · planning → executing · execute-step1-trap6-wget-fix3h ago

    Applied the iter-6-planned Trap #6 fix (coding only — no Docker build/run this turn, per the planning->executing rule). Single-file edit to the Dockerfile: added `wget` and `unzip` to the apt-get --no-install-recommends list (right after ca-certificates) with a Trap #6 comment, because the nvidia/cuda:12.4.0-devel-ubuntu22.04 base ships NEITHER and install_dpvslam.sh shells out to both (wget for eigen.zip/models.zip/ORBvoc.txt.tar.gz; unzip for eigen+models) — the iter-6 build died at install L64 with `wget: command not found` (exit 127). This is the minimal unblock: the iter-6 build already proved everything BEFORE the wget call is GREEN (base pull, apt layer, torch 2.3.1+cu121 + torch_scatter + python deps, DPVO clone @ 859bbbf), so only the install-script layer onward re-runs. Honest carry-forward (now the real risk): the nvcc sm_86 CUDA-extension compile (install Step 7) and the probe_no_cuda_ipc.py BUILD GATE have STILL never been exercised and remain unproven — they are the unknowns for the next build. Read v3-B memory files this turn (CLAUDE.md operating manual incl. Trap #6, pillar charter) and appended the iter-7 CHANGELOG entry before this write-back. Status -> executing. Next transition (executing->executing) runs `docker compose build vision-robotics` to GREEN, logs -> runs/build_iter7.log; per the Trap #6 gotcha, success is read from the log (`BUILD_EXIT=` / `failed to solve:`), not the wrapper's outer exit code. Adding to the apt layer invalidates the install-script cache layer, so torch re-downloads (~several min) before reaching the new CUDA-compile + probe stages.

    Modified: algorithm/vision-robotics/missions/M-007/Dockerfile, algorithm/vision-robotics/missions/M-007/CHANGELOG.md

18. iter 6 · executing → planning · execute-failed-step1-build8h ago

    Ran the real Step-1 gate `docker compose build vision-robotics` (logs -> runs/build_iter5.log). The Trap #5 compose-schema reject is gone, so the build STARTED for the first time and got meaningfully far: nvidia/cuda:12.4.0-devel base pulled, apt layer built, torch 2.3.1+cu121 + torch_scatter + the python deps (evo, kornia, opencv-headless, numpy==1.26.4, ...) all installed GREEN, and DPVO cloned --recursive + checked out at the pinned SHA 859bbbf. It then FAILED at install_dpvslam.sh line 64 with `wget: command not found` -> RUN exit code 127. Root cause: the Dockerfile apt list installs git/ca-certificates/build-essential/cmake/ninja/python/eigen/GL but NOT wget or unzip, and install_dpvslam.sh shells out to wget (eigen.zip L64, models.zip L84, ORBvoc.txt.tar.gz L93) and unzip (eigen L65, models L85). Neither ships in the cuda devel base (tar IS present, so L94 would have been fine). This is a PRE-CUDA-COMPILE failure: the expensive nvcc sm_86 extension build (install script Step 7) and the probe_no_cuda_ipc.py BUILD GATE were never reached and remain unproven — the real risk for the next build. Reverted executing -> planning per the run-step error rule (no fix coded this turn). Next iteration (planning -> executing) adds `wget unzip` to the Dockerfile apt-get list (1-file edit, v3-D) and re-runs the build -> runs/build_iter6.log; adding to the apt layer invalidates the install-script layer so torch re-downloads (~several min) before reaching the new CUDA-compile + probe stages. Honest carry-forward (pinned as Trap #6 in CLAUDE.md): the background-build wrapper printed BUILD_EXIT=1 but the task-completion status was exit 0 (the trailing `tee` succeeded) — always read the log for BUILD_EXIT=/`failed to solve:`, never trust the outer task status.

    Modified: algorithm/vision-robotics/missions/M-007/CHANGELOG.md, algorithm/vision-robotics/missions/M-007/CLAUDE.md

    Created: algorithm/vision-robotics/missions/M-007/runs/build_iter5.log

19. iter 5 · planning → executing · execute-step1-compose-trap5-fix17h ago

    Applied the iter-4-planned fix for Trap #5 (coding only — no Docker build/run this turn, per the planning->executing rule). Single-file edit to docker-compose.yml: removed `build.gpus: all` (the host's Docker Compose v2.37.1-desktop.1 hard-rejects the key with 'services.vision-robotics.build Additional property gpus is not allowed' — the Desktop-bundled schema lacks the build.gpus key entirely, so `docker compose build` refuses to run at all) and replaced it with an explanatory Trap #5 comment block. Kept BOTH the runtime GPU reservation under deploy.resources AND the Dockerfile `ENV TORCH_CUDA_ARCH_LIST=8.6` cross-compile pin intact — so the sm_86 DPVO CUDA kernels still build deterministically without a build-time GPU (setup.py never autodetects when the arch list is set), satisfying spec hard-constraint #3's INTENT (sm_86 kernels actually built for the 3080) while relaxing its literal 'gpus: all under build' wording (to be surfaced in the postmortem as an honest caveat). Validated with `docker compose config -q` -> clean (printed COMPOSE_SCHEMA_OK), confirming the schema hard-reject is gone before claiming the fix. Appended the iter-5 entry to CHANGELOG.md (v3-B). Status -> executing. Next transition (executing->executing) runs the real Step-1 gate: `docker compose build vision-robotics` to GREEN (nvcc compile of the DPVO sm_86 CUDA extensions + the probe_no_cuda_ipc.py BUILD GATE), logs -> runs/build_iter5.log.

    Modified: algorithm/vision-robotics/missions/M-007/docker-compose.yml, algorithm/vision-robotics/missions/M-007/CHANGELOG.md

20. iter 4 · executing → planning · execute-failed-step1-build17h ago

    Ran the Step-1 build `docker compose build vision-robotics` (logs -> runs/build_iter3.log). It FAILED INSTANTLY at compose YAML schema validation, before any image layer built: `services.vision-robotics.build Additional property gpus is not allowed`. `docker compose config --quiet` reproduces it deterministically. Root cause: host Compose is v2.37.1-desktop.1 (>= v2.30, so iter-2 EXPECTED `build.gpus` to be honored-or-silently-ignored); the real answer is NEITHER — the Docker-Desktop-bundled JSON schema does not contain the `build.gpus` key at all and HARD-REJECTS the whole file, so `docker compose build` refuses to run. This is the key being absent from the schema ('Additional property gpus is not allowed'), not a value-format problem (`all` vs the long-form device list). Per the run-step error rule I reverted executing -> planning rather than coding a fix this turn. The fix is small and already de-risked: the Dockerfile pins `ENV TORCH_CUDA_ARCH_LIST=8.6`, so DPVO setup.py cross-compiles the sm_86 kernels deterministically with NO build-time GPU (it never autodetects when the arch list is set) — the build-time GPU was belt, the arch-pin is sufficient suspenders. Next iteration (planning -> executing) removes `build.gpus: all` from docker-compose.yml (1-file edit, keeping the runtime `deploy.resources` GPU reservation intact) and re-runs the build -> runs/build_iter4.log. Honest note for the postmortem: this relaxes spec hard_constraint #3's literal 'gpus: all under build' wording while satisfying its intent (sm_86 kernels actually built for the 3080); pinned as Trap #5 in the sandbox CLAUDE.md so it is not re-discovered.

    Modified: algorithm/vision-robotics/missions/M-007/CHANGELOG.md, algorithm/vision-robotics/missions/M-007/CLAUDE.md

    Created: algorithm/vision-robotics/missions/M-007/runs/build_iter3.log

21. iter 3 · executing → executing · execute-step1-dpvslam-install-coding18h ago

    Step-1 CODING transition (no Docker run this turn — that is the next executing step). FIRST and gating: verified the single-process invariant upstream BEFORE pinning a commit (spec hard_constraint #1 / the M-006 lesson). Audited princeton-vl/DPVO @ 859bbbfdac6c6185f345003b3c473901fcd13ace via the GitHub API + raw source: multiprocessing IS used (image-reader Process in demo.py/stream.py/evaluate_kitti.py, DBoW retrieval Process, image_cache Pool, and the loop-closure PGO mp.Pool in long_term.py), but EVERY process boundary passes only CPU/numpy — decisively, long_term.py does `pred_poses = pp.SE3(...).Inv().cpu()` and `SE3_to_Sim3(Gij).data[0].cpu()` before apply_async(run_DPVO_PGO,...), and results return via Manager().Queue() and are .cuda()'d only AFTER retrieval in the main process. No live CUDA tensor crosses a process boundary, so DPV-SLAM++ does NOT trip the WSL2 CUDA-IPC wall that killed M-006. Gate PASSES; pinned the SHA. Authored 3 files (within v3-D ≤3): (1) src/setup/install_dpvslam.sh — build-time installer: torch 2.3.1+cu121 + torchvision + torch_scatter (pyg wheel) per upstream environment.yml (py3.10, numpy==1.26.4), clone --recursive at the SHA, eigen 3.4.0 into thirdparty, build DPVO CUDA exts + ./DPRetrieval (DBoW) with --no-build-isolation (Trap #5), skip the Pangolin DPViewer (no X display — we render overlay.html offline), download ONLY models.zip (dpvo.pth) not TartanAir/movies (disk budget), download ORBvoc.txt (download_models_and_data.sh omits it — needed for loop closure), warm the kornia DISK+LightGlue cache (hidden runtime net dep), GPU-free import sanity (Trap #12). (2) src/setup/probe_no_cuda_ipc.py — deterministic static audit (grep the pinned source, allowlist the 7 audited mp call sites, fail on any new unaudited call site, re-assert the long_term.py .cpu() fences); wired as a hard BUILD GATE so the image cannot go GREEN if the invariant breaks (v3-C: deterministic, not an LLM/log-regex judgment). (3) Dockerfile — replaced the iter-2 placeholder with ARG DPVSLAM_COMMIT + COPY/RUN of the install script then the probe gate. Also corrected 3 upstream traps in CLAUDE.md that the original code_plan got wrong (KITTI uses image_2 COLOR not image_0 grayscale; loop closure needs ORBvoc.txt at the abs path /opt/DPVO/ORBvoc.txt since runtime cwd is /workspace; kornia weights are a hidden runtime network dependency) — these will save Step-2/Step-3 iterations. Status stays executing; next transition runs `docker compose build vision-robotics` to GREEN (logs -> runs/build_iter3.log).

    Modified: algorithm/vision-robotics/missions/M-007/src/setup/install_dpvslam.sh, algorithm/vision-robotics/missions/M-007/src/setup/probe_no_cuda_ipc.py, algorithm/vision-robotics/missions/M-007/Dockerfile, algorithm/vision-robotics/missions/M-007/CLAUDE.md

22. iter 2 · planning → executing · execute-step0-docker-infra4h ago

    Executed the remaining Step-0 work: authored docker-compose.yml + Dockerfile at the sandbox root (coding only — no Docker run this turn, per the planning->executing rule). Dockerfile: FROM nvidia/cuda:12.4.0-devel-ubuntu22.04 (devel = nvcc present for the lietorch/DPVO CUDA-extension compile — M-006 Trap #4 lesson), ENV TORCH_CUDA_ARCH_LIST=8.6 set IN THE DOCKERFILE so the sm_86 kernels cross-compile at build time (M-006 Trap #1a — compose environment: never reaches the build), plus WANDB_MODE=disabled and the python/build-toolchain/eigen3/GL system deps; a commented COPY/ARG/RUN block marks exactly where Step-1's install_dpvslam.sh hooks in, with reminders to use --no-build-isolation (Trap #5) and a GPU-free build-time sanity import (Trap #12). docker-compose.yml: service name `vision-robotics` (MUST match the oracle's `docker compose run --rm vision-robotics ...`, Step 6), host-first `build.gpus: all` under build: (spec hard-constraint #3 / feedback_host_hardware_first — the deliberate inverse of M-006's GPU-free build) PLUS a runtime GPU reservation, `.:/workspace` bind-mount as the sole writable surface (v3-A). Deliberately NO ipc:host/shm: DPV-SLAM++ is single-process, so M-006's CUDA-IPC remedy and the WSL2 wall it fought (Trap #14) do not apply. Deferred to Step 1 per spec: exact torch wheel (cu118 vs cu124) + pinned DPV-SLAM commit SHA, both read from upstream README. Marked Step 0 done in code_plan; status -> executing. Next (executing->executing): Step 1 — clone DPV-SLAM at a pinned SHA, author install_dpvslam.sh, run `docker compose build vision-robotics` to GREEN.

    Modified: algorithm/vision-robotics/missions/M-007/Dockerfile, algorithm/vision-robotics/missions/M-007/docker-compose.yml

23. iter 1 · queued → planning · preflight+elaborate-code-plan18h ago

    Host pre-flight PASSED before entering the plan/execute loop (M-003 P1 patch). Probed cheapest-first: nvidia-smi reports RTX 3080 Laptop GPU, compute_cap 8.6 (sm_86), 16384 MiB — meets the host_requirements gate (sm_86, >=8GB VRAM). Critically, ran `docker run --rm --gpus all nvidia/cuda:12.4.0-base-ubuntu22.04 nvidia-smi` and the GPU is visible INSIDE the container with sm_86 — this is the precise WSL2/container-GPU concern that the host-first BuildKit policy depends on, and it works (a cached base image made the probe near-free). nvcc is absent on the host but that is expected and non-blocking: it is supplied by the CUDA-devel base image at docker-build time and will gate in Step 1's `docker compose build`. The CUDA-IPC smoke is explicitly non-gating for M-007 (DPV-SLAM++ is single-process by design — the deliberate counter to M-006's MonoGS). The spec's literal preflight_command imports torch on the host, which fails only because torch lives in the container, not the host — the wrong layer to gate on; the gating intent (sm_86 GPU reachable from a container) is fully satisfied. Elaborated approach.code_plan: confirmed the sandbox skeleton + pillar/sandbox CLAUDE.md + CHANGELOG were already materialised by the mission-skill bootstrap, so the only remaining Step-0 work is authoring docker-compose.yml + Dockerfile (FROM nvidia/cuda:12.4.0-devel-ubuntu22.04 to get nvcc, ENV TORCH_CUDA_ARCH_LIST=8.6, gpus:all under build + deploy.resources). Next transition (planning -> executing) will author those two files as the first one-task code step.

    Modified: algorithm/vision-robotics/missions/M-007/CHANGELOG.md

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