Nav pt3: Clean up PGO + Tests#2099
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Native module (cpp/main.cpp) now publishes two new streams on every
keyframe: GraphNodes3D for keyframe optimized poses, LineSegments3D for
odometry (traversability=1.0) and loop-closure (0.4) edges. Both wire
through SimplePGO::keyPoses() + historyPairs() — no changes needed to
simple_pgo.{h,cpp} since the accessors already exist. Native binary
rebuilt cleanly via nix build .#default --no-write-lock-file.
Python (pgo.py) declares matching pgo_graph_nodes / pgo_graph_edges Out
streams so the rerun bridge auto-discovers and logs them.
nav_stack_rerun_config() now picks _agentic_debug_rerun_blueprint when
agentic_debug=True — an rrb.Horizontal layout with a 3D pane and a
dedicated top-down pane (both Spatial3DView over origin="world", named
"3D" and "top_down" so dimos-viewer persists camera state separately).
demo_better_pgo_viz.py composes the cross-wall sim blueprint with
agentic_debug=True so the new layout + pose graph render together. Used
for manual screenshot validation.
Adds visual_override entries for world/pgo_graph_nodes and world/pgo_graph_edges that mirror the existing FAR pattern: when agentic_debug=True, the PGO pose graph renders at z=_AGENTIC_DEBUG_LIFT (3.0m) instead of the default 1.7m, with slightly larger node radii (0.15) and edge thickness (0.06) so the green keyframe trajectory stands out clearly above the terrain cloud in the top-down pane. Verified visually via demo_better_pgo_viz with the cross-wall sim — green keyframe nodes + edges are now plainly identifiable above terrain in both the 3D and top_down rerun panels.
rerun's Spatial3DView doesn't have a top-down camera API, so the "top_down" pane introduced in a7a9be9 was just a duplicate 3D view. Drop _agentic_debug_rerun_blueprint and use _default_rerun_blueprint unconditionally — the agentic_debug lift on visual_override is what actually makes the pose graph and nav markers readable from any angle.
C++ side (main.cpp): when searchForLoopPairs sets m_cache_pairs (i.e. this keyframe will be incorporated into iSAM2 with a loop factor), snapshot the current global poses before smoothAndUpdate. After the update, build a nav_msgs::Path-encoded LoopClosureDeltas message: position = post.t - r_delta * pre.t, orientation = quaternion(post.R * pre.R^T). Publish on the new pgo_loop_closure topic. Stderr logs the event count for live observability. Python side (pgo.py): declare pgo_loop_closure: Out[NavPath] so the new topic is registered alongside corrected_odometry/pgo_tf/etc. Slow test (test_pgo_loop_closure.py): replays og_nav_60s through the native binary with permissive thresholds (loop_time_thresh=5s, min_loop_detect_duration=1s, loop_search_radius=2m, loop_score_thresh=0.5) so the recording reliably triggers loop closures. Subscribes to pgo_loop_closure, logs each event the moment it arrives (event #, poses_length, frame_id, first delta), and after the run validates each event has >0 poses, finite translations (<100m), and unit-norm quaternions (drift <0.05). Stdout from a run shows 19 events, sizes 10..35, max |t|=0.0013m, max |q|-1|=1e-6 — exactly the small-nudge profile expected from a self-consistent recording.
Replaces the kdtree-on-keyframe-positions loop search with a Scan
Context (Kim & Kim 2018) descriptor-based pipeline:
1. addKeyPose now also caches a polar-binned (20 rings × 60 sectors)
max-z descriptor + the per-row mean "ring key" for each keyframe.
The descriptor is appearance-based and pose-independent, so it
keeps working even when odometry has drifted enough that the new
keyframe is no longer "near" its old neighbours in pose-space.
2. searchForLoopPairs first asks Scan Context for a candidate:
ring-key L2 distance ranks all past keyframes, top-K are scored
by column-shifted cosine distance on the full descriptor, the
best below the threshold (default 0.4) is the candidate. The
winning column shift is also converted to a yaw rotation and used
to seed ICP, which dramatically improves convergence on revisits
that arrive at a different heading from the original pass.
3. Position-based search is retained as a fallback when SC is
disabled or finds nothing, so existing behaviour is preserved.
Replaces ~50 lines of position-search with ~30 lines of SC retrieval
in searchForLoopPairs; new scan_context.{h,cpp} (~150 lines, MIT
attribution to upstream irapkaist/scancontext concepts but no source
copied) implements the descriptor + distance.
Side-effect: this makes on-start relocalization a small follow-up
addition — descriptors + ring-keys + poses are now per-keyframe state
that can be serialised, and the SC search path already does
"appearance-based pose recovery without an initial pose guess."
Verified via test_pgo_loop_closure.py: 17 loop-closure events fired
across the og_nav_60s rosbag (was 19 with naive position search; SC
is more selective and rejects two borderline-position matches that
weren't actually visual revisits). All events have valid shape + tiny
quaternion/translation deltas as expected for a self-consistent bag.
…n search misses Adds CLI args to expose Scan Context config on the native binary (--use_scan_context, --sc_n_rings, --sc_n_sectors, --sc_max_range_m, --sc_top_k, --sc_match_threshold). New slow test test_pgo_synthetic_drift.py: - Synthesises a 4-wall point-cloud room with two distinctive interior columns (so the scene isn't rotationally symmetric). - Generates an out-and-back trajectory: drives east 8m then returns to the origin, heading unchanged. - Injects DRIFT_AT_REVISIT_M = 5m of additive y-drift into the reported odometry, ramped linearly with travelled distance. The body-frame scan stays byte-identical between first and second visit (same true sensor view of the same scene); the odom pose at revisit is 5m offset. - Runs the native PGO binary twice over the same input: * use_scan_context=true → expect ≥1 loop event * use_scan_context=false → expect 0 loop events (drift >> 1m radius) - Dumps PGO stderr after each run for diagnostics. Result: SC fires 10 loop closure events on the synthetic trajectory; position-based search fires 0 — exactly the demonstration of why we swapped to appearance-based place recognition. Both assertions pass. Verifies the core SC value prop: appearance-based place recognition doesn't depend on the (drifted) pose, so it keeps working when the odometry has wandered far enough that the kdtree-on-positions search no longer finds neighbours.
Test files now use setup_logger() / logger.info(...) per the fix_nits rule "no print() calls in tests; use logging if diagnostics are genuinely needed." Matches the existing test_pgo_rosbag.py convention. Also drops the now-unused sys import. Also clears a stale docstring on demo_better_pgo_viz.py: it claimed the demo enabled a "horizontal 3D + top-down panes" layout, which was reverted in 1801759 — rerun's Spatial3DView didn't support an initial camera angle (rrb.EyeControls3D existed at the time but wasn't used). The remaining value of agentic_debug=True is the visual override lift, which the new docstring describes accurately. No behavioural change. Tests still pass.
Sweep over names introduced by the better_pgo work that hit fix_nits
"expand mod -> module" rule:
- scan_context: cfg -> config (param + 12 call-sites); d (return val) ->
descriptor in make_descriptor/make_ring_key/make_sector_key; pt -> point
in the descriptor build loop; zf -> point_z (float cast); q_col/c_col
-> query_column/candidate_column; q_norm/c_norm -> query_norm/
candidate_norm; cj -> shifted_j; d (in best_distance return loop) ->
distance with min_distance for the running best.
- simple_pgo: desc -> descriptor on the per-keyframe cache; k ->
top_k_count for the partial-sort bound; structured-binding `auto [d,
shift]` -> `auto [distance, shift]`.
- main.cpp: kp -> keyframe; ps -> pose_stamped (build_graph_nodes and
build_loop_closure_deltas); a/b -> start/end and p1/p2 ->
start_pose/end_pose in append_segment; n -> count for the loop bound;
lc_msg -> loop_closure_msg at the publish site.
- tests: ps -> pose in the validate loop (test_pgo_loop_closure);
c,s -> cos_yaw,sin_yaw in _yaw_rotation (test_pgo_synthetic_drift).
Names that intentionally stay short are the math-convention ones:
r/t for SE(3) rotation+translation, q for quaternion, i/j as loop
indices, idx as keyframe index, ts as timestamp, dt for time delta,
tx/ty/tz/qx/qy/qz/qw for component decomposition. The fix_nits rule
calls out mod/lc as the target pattern; expanding the math-notation
names would make the code less readable, not more.
Also drops one section-label comment ("# Log each event the moment it
arrives.") whose adjacent function name already conveys the same and
one in-loop "# node_type 1 = odom/robot" that repeats info already
stated in the function-level docstring.
Native binary rebuilt + slow test still passes (17 events, all valid).
Drops in the wiring for evaluating the PGO native module on KITTI-360. Cannot run end-to-end yet — the dataset is gated behind a registered login at cvlibs.net so the data download is a manual user step. What's here: - kitti360_loader.py: parses the KITTI-360 directory layout (data_3d_raw + data_poses + calibration); composes per-frame lidar→world pose by chaining cam0_to_world ⊕ inv(velo_to_cam). Exposes a frame iterator + scan_xyz(frame_id). - loop_groundtruth.py: LCDNet/KITTI-convention groundtruth (≥50 frame gap, ≤4m radius), order-agnostic scoring of detected pairs. - run_kitti360_benchmark.py: argparse CLI, spawns the native binary on private LCM topics, plays (registered_scan, odometry) from disk, subscribes to pgo_graph_edges to extract loop-closure pairs (via traversability ≈ 0.4 segments) and pgo_loop_closure for delta event counts. Writes JSON. - README.md: download instructions for the official "Test SLAM 3D" 12 GB package, published SOTA reference numbers from LCDNet + ISC papers (LCDNet 0.91-0.93 AP, Scan Context 0.62-0.78 AP), expected ballpark for our minimal SC port.
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Contributor
Greptile SummaryThis PR expands the PGO (pose-graph optimization) module with Scan Context place recognition, a new
Confidence Score: 4/5Safe to merge for the test harness and message-type work; the C++ loop-closure path has a known recall regression where a Scan Context candidate rejected by ICP silently prevents the position-based fallback from running. The SC false-positive→ICP-rejection path that silently skips the position fallback remains unaddressed in searchForLoopPairs. Several issues from the previous review were resolved: RosbagScanOdomPlaybackModule gained proper try/except/finally guarding, all six SC config fields are now in PGOConfig, the port-type mismatch is fixed, and the loop_score_tresh typo is corrected throughout. simple_pgo.cpp (SC→ICP→no-position-fallback flow) and all_blueprints.py (test/eval harness modules in the production registry). Important Files Changed
Sequence Diagram%%{init: {'theme': 'neutral'}}%%
sequenceDiagram
participant Replay as SyntheticLockstepReplay
participant PGO as PGO (C++ binary)
participant GraphCapture
Replay->>PGO: odometry (drifted pose)
Replay->>PGO: registered_scan (world-frame points)
PGO->>PGO: addKeyPose build SC descriptor
PGO->>PGO: searchForLoopPairs (SC ICP gate)
alt SC candidate found and ICP passes
PGO->>PGO: smoothAndUpdate (iSAM2)
PGO->>GraphCapture: loop_closure_event (GraphDelta3D)
end
PGO->>GraphCapture: pose_graph (Graph3D, all keyframes)
PGO->>Replay: corrected_odometry (ACK)
Replay->>PGO: next scan (lockstep)
%%{init: {'theme': 'base', 'themeVariables': {"darkMode": true, "background": "#0d1117", "primaryColor": "#21262d", "primaryTextColor": "#e6edf3", "primaryBorderColor": "#8b949e", "lineColor": "#8b949e", "textColor": "#e6edf3", "edgeLabelBackground": "#161b22", "actorBkg": "#21262d", "actorBorder": "#8b949e", "actorTextColor": "#e6edf3", "actorLineColor": "#8b949e", "signalColor": "#8b949e", "signalTextColor": "#e6edf3", "noteBkgColor": "#373320", "noteBorderColor": "#d4a72c", "noteTextColor": "#f0e6c0", "labelBoxBkgColor": "#21262d", "labelBoxBorderColor": "#8b949e", "labelTextColor": "#e6edf3", "loopTextColor": "#e6edf3", "activationBkgColor": "#30363d", "activationBorderColor": "#8b949e"}}}%%
sequenceDiagram
participant Replay as SyntheticLockstepReplay
participant PGO as PGO (C++ binary)
participant GraphCapture
Replay->>PGO: odometry (drifted pose)
Replay->>PGO: registered_scan (world-frame points)
PGO->>PGO: addKeyPose build SC descriptor
PGO->>PGO: searchForLoopPairs (SC ICP gate)
alt SC candidate found and ICP passes
PGO->>PGO: smoothAndUpdate (iSAM2)
PGO->>GraphCapture: loop_closure_event (GraphDelta3D)
end
PGO->>GraphCapture: pose_graph (Graph3D, all keyframes)
PGO->>Replay: corrected_odometry (ACK)
Replay->>PGO: next scan (lockstep)
Reviews (74): Last reviewed commit: "ci: re-trigger ci (pull_request trigger ..." | Re-trigger Greptile |
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- run_kitti360_benchmark: type the scipy Rotation.as_quat result to silence no-any-return. - demo_better_pgo_viz: annotate build_blueprint() -> Blueprint.
shift comes from best_distance, which scans [0, n_sectors-1], so the raw yaw is in (-2pi, 0] and `yaw > M_PI` can never fire. Only the negative-wrap guard is needed to normalize into [-pi, pi].
dimos/ disallows __init__.py files (test_no_init_files) — the empty one in pgo/benchmark/ slipped in with c7fd631 and was tripping the 3.14 test job.
mypy can't infer parameter types on a lambda subscribed to LCM; lift the body into a tiny factory function with explicit Callable[[str, bytes], None] signature so the lint job passes.
sklearn doesn't ship a py.typed marker; the new place_recognition_ap benchmark is the only sklearn user in dimos, so a per-import ignore is the smallest fix to unstick the lint job.
Naming sweep across dimos/navigation/nav_stack/modules/pgo per fix_nits review conventions: expanded short locals (frac, true_pos, dt, ts, pos, yaw, msg, sub, idx, fid, gt, cfg, desc, dots, sims, dists, pa/pb, …) to full descriptive names in the benchmark scripts, the synthetic-drift test, and the loop-closure test. Greptile P1 fixes on PR #2099: - c2: benchmark sender and the timestamp_ms→frame_id cache now share a single _compute_send_timestamps source of truth. Previously the cache was keyed by raw KITTI timestamps while the runner sent max(raw_ts, 1.0 + index*0.001), so early-frame endpoints in PGO loop edges never matched the cache and were silently dropped — deflating recall without any warning. - c3: load_kitti360_sequence now raises on .bin/timestamps.txt length mismatch instead of silently leaving timestamps={}, which previously caused the benchmark to report recall=0 with no indication that the timestamp file was unusable. - c1 follow-up: rename the misspelled C++ field loop_score_tresh → loop_score_thresh in simple_pgo.{h,cpp} and main.cpp. The CLI flag was always spelled correctly; this is cosmetic but removes the source of confusion greptile (correctly) flagged. New regression test (test_pgo_synthetic_drift.py): test_scan_context_catches_reverse_loop drives the synthetic robot out 8m facing east, turns 180°, and drives back facing west. Body-frame scans on the return leg are rotated 180° relative to outbound, so this exercises the init_guess fix in searchForLoopPairs (yaw rotated about the source keyframe instead of the world origin). Reverting that fix reproduces the failure. New runners: - benchmark/place_recognition_ap.py: apples-to-apples place-recognition AP eval against published Scan Context numbers. Reports AP=0.97-0.98 on seq 02/04/08 of the Test SLAM split, with precision 1.000 at the Kim & Kim 0.13 threshold. - benchmark/smoke_test.py: ~10s liveness probe that subscribes to all six PGO output topics, captures stderr, and prints per-topic message counts plus a one-line verdict — used to distinguish "PGO crashed" vs "no keyframes" vs "no loops" vs "alive" during debugging. The benchmark runner also now captures PGO's stderr and dumps it behind --print-stderr; previously its diagnostic prints (keyframes, loop-closure events) were discarded.
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Relocate the make_gt_china GT pipeline into dimos2 as a self-contained `pgo/` package so it no longer depends on the dimos3 jnav clone. Vendors the helpers it needs under pgo/utils/ (recording_db, trajectory_metrics, voxel_map, apriltags); everything else resolves from this clone's own `dimos` package. - detect_tags.py: build the unfiltered raw AprilTag stream (with per-detection gate diagnostics). Args: --rec (required), --camera, --tag-size, --dict, --intrinsics, --out. - post_process.py: two-stage solve (quality-weighted tag PGO + ICP loop-closure refinement) -> <out>_odometry / <out>_lidar. --rec now required (no china default). Args for stream sources: --lidar, --odom, --tags, --out, --suffix, --no-icp. Emits a <out>_lidar.pc2.lcm log (--no-lcm) and builds+opens a comparison rrd (--no-rrd). Flushed progress logging on the slow ICP stages. - make_rrd.py: parameterized comparison-rrd builder (importable build()). Verified numerically identical to the original dimos3 run on china_office1 (262 tag factors, 44526 ICP closures, 8.8 m max shift).
- Stamp each corrected PointCloud2 (m.ts) before append: dimos2's PointCloud2.lcm_encode does int(self.ts) with no None-guard (dimos3's has one), so the db-write codec crashed on the lidar stage. Latent dimos2 bug; worked around here. - The .pc2.lcm is now a single aggregated cloud (voxel-downsampled + statistical-outlier-removed) instead of one event per scan. Memory bounded via incremental per-chunk voxel collapse; intensity preserved through open3d's color-channel averaging. New flag: --lcm-voxel (default 0.05).
| @@ -135,28 +142,28 @@ class LcmCollector: | |||
| """Subscribes to an LCM topic and collects decoded messages with timestamps.""" | |||
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| topic: str | |||
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msg_type rename leaves test_far_planner_rosbag.py broken
This PR renames the LcmCollector field msg_type → message_type. Three test files in the diff were updated accordingly, but test_far_planner_rosbag.py (which was not in this diff) still passes msg_type=PointStamped at line 220. Because LcmCollector is a @dataclass, the old keyword argument is now invalid, and that test will raise TypeError: LcmCollector.__init__() got an unexpected keyword argument 'msg_type' the moment the fixture runs.
CI's mypy runner has no gtsam installed, so the bare '# type: ignore[import-untyped]' on these two imports didn't cover the import-not-found error -> lint failed and fail-fast cancelled the whole test matrix. Match the repo's established '# type: ignore[import-not-found,import-untyped]' pattern.
The repo's no-sections policy test forbids '# --- section ---' / '# === section ===' style comment banners. The relocated pgo/ scripts carried several from the original make_gt_china.py, and the PR's own ivan/unrefined pgo modules + vendored apriltags had a few too. Convert them to plain comments / drop pure separators.
The branch renamed the pgo blueprint (pgo.pgo.PGO -> unrefined_pgo.module.PGO) and added rate-replay, but the generated registry was never refreshed. Regenerate it.
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Relocalization loads the premap via PointCloud2.lcm_decode(read_bytes()), which expects a bare lcm_encode() message (matching dimos map --export). post_process wrapped it in an lcm.EventLog, so the file failed to decode.
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Restore detect_tags/post_process/make_rrd under modules/pgo/scripts/ (recovered from history, rewired onto eval_utils, sys.path hack removed). Add a navigation map-postprocessing guide.
…t replay Cross-talk on the default LCM multicast channel silently wedged every lockstep eval: a live odometry message from another dimos clone, stamped with wall-clock time, poisoned PGO's monotonic out-of-order guard so every replayed (older-timestamp) scan was dropped and the replay timed out on each one forever. Give each eval its own multicast port via LCM_DEFAULT_URL — set pre-launch in eval_all's cell subprocess (so the forkserver workers inherit it) and via re-exec in eval.py for direct runs. Omit a literal "?ttl=0" query string: it hangs the worker transport setup, and udpm already defaults to ttl=0 so loopback isolation is preserved. Also gate PGO's debug scan prints behind g_debug so they can be toggled.
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…son report - scripts/add_april.py: one step builds raw_april_tags (unfiltered) + april_tags (gated) and writes an april_tags section (filter_parameters + per-tag revisit result, incl. all_unfiltered_tag_ids) into each recording's summary.json; --summary (read-only), --output, --dynamic (drop moving tags from filtered). - apriltags.py: relax gates to post_process values + make them the single source of truth; refactor into reusable detect_raw_detections / gate_detections / write_april_streams / ensure_april_streams / gate_params. - post_process.py: import gate thresholds from apriltags (drop the duplicate GATE). - eval.py: include the full corrected_trajectory in each cell summary. - eval_results/report.md + reference_comparison.md (china_office1 added) + charts. - docs/map_postprocessing.md: cover add_april + the summary.json april_tags section.
…_process needs them)
…/open3d/cv2 lack stubs)
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Closes #2007
Changes
How to Test
without benchmark
With benchmark
The benchmark is KITTI 360 - a standard dataset but its stuck behind a registration page (no public download). Its also 12Gb. https://www.cvlibs.net/datasets/kitti-360/user_login.php
There's no good 100% public loop closure benchmark that I could find.
Not in this PR
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