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WalkthroughThe change increases AMCL laser-beam usage and documents pose-correction measurement. It also adds simulator teardown, restart verification, and single-launch guidance. ChangesGuidance updates
Suggested reviewers: Priority: ⬇️ Low Merge Risk: ⚪ Minimal · up to The change enables use of the full scan and provides consistent measurement and deployment guidance; no concrete merge-blocking risk remains. 🚥 Pre-merge checks | ✅ 4✅ Passed checks (4 passed)
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In `@AGENTS.md`:
- Around line 232-239: Update the measurement description preceding the compose
example to state that static-map behavior is evaluated by comparing two
recomposed estimates, p_old and p_new, using the same odom-to-base pose;
alternatively, add and use the missing ground-truth input so the text and
example describe the same calculation.
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AGENTS.mdsrc/hangar_sim/CMakeLists.txtsrc/hangar_sim/params/nav2_params.yamlsrc/hangar_sim/test/test_amcl_scan_coverage.py
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bkanator
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coderabbitai[bot]’s stale review
September 17, 2026 14:22
stale, and that file doesn't exist now
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A misconfiguration fix. It is explicitly NOT the answer to "the map jumps", and the measurement below is the reason: at the robot, the map was never moving as far as the number everyone has been quoting. READ THIS FIRST IF YOU MEASURE THIS CONFIGURATION `map -> odom`'s translation component is where the ODOM ORIGIN sits in the map, not how far anything moved near the robot. An AMCL update is a rigid transform change, so a point p is displaced by `dt + (R_new - R_old) p`. On this route the robot is up to 27 m from the odom origin, so a yaw correction of 0.008 rad - under half a degree - reads as a 0.16 m `map -> odom` step while displacing the robot 2 cm. The five largest steps in the control run were 0.124-0.161 m and moved the robot 0.005-0.024 m. Re-checked against the older reference run, its headline 0.316 m step is 0.132 m at the robot; that 0.132 m is the largest at-robot displacement anyone has actually measured here and is the honest upper bound. AGENTS.md carries the full note, including how to compute it without tripping over asynchronous TF lookups. The correct claim is that at the robot the motion is FAR smaller than the `map -> odom` figure suggested - not that it is zero under all conditions. THE ACTUAL DEFECT THIS FIXES max_beams was 60 against a 723-ray /scan_merged, which is far worse than the 8% it looks like. beluga subsamples with take_evenly() over the RAW ranges array, BEFORE invalid returns are filtered out (beluga_ros/laser_scan.hpp), and the hangar scans are deliberately sparse - open space plus self-hit filtering leave finite returns surrounded by inf, which is why the merger's shadow and average filters are disabled. Read live off the running stack at the robot's spawn pose, where structure is BEST: 283 of 723 rays are finite and in range, and take_evenly(60) keeps just 23 valid points of the 60 it samples. AMCL was weighting its particles on roughly two dozen points, and fewest where returns are sparsest. That justification stands on its own without any claim about jumping. The likelihood field sums pz^3 per beam rather than multiplying, so the particle weight is essentially a count of matched beams; counting more of them is better conditioned, without the over-confidence that beam subsampling guards against in a true product-of-likelihoods model. WHAT IT MEASURED Two runs per setting on the acceptance route, port-lane stretch (y=5..22): `map -> odom` steps over 0.10 m fell from 13 and 16 to 8 and 9, and p99 from 0.109 and 0.122 m to 0.089 and 0.093 m. Largest single step did not improve. At the robot, whole route, both settings stay under 0.09 m with no step over 0.10 m. Limits of that evidence, stated so nobody over-reads it: - these runs sat at real-time factor 0.940/0.944 against the reference run's 0.895, so wheel odometry inflated +6.4% rather than +11.8% and AMCL had about half as much drift to correct; - the reference run's magnitude was therefore NOT reproduced, and no claim is made that it was. The A/B is internally matched (same stack recipe, minutes apart, matched RTF), which is what the comparison rests on. Filter CPU: no detectable cost, nav2 container at 27% of one core at 60 beams and at 725. That is a lifetime-average %cpu, so it bounds the difference at roughly 1% of a core rather than resolving it precisely; the arithmetic agrees, 1000 particles x 725 beams being 725k grid lookups about once a second. TWO LEVERS REJECTED ON MEASUREMENT, recorded so they are not re-proposed selective_resampling made it worse. It was the only setting tried that produced steps over 0.25 m at all: 0.439 m at the odom origin, 0.283 m at the robot, with 5 at-robot steps over 0.10 m, against zero across four runs without it. Coasting without resampling lets the cloud widen while the scan is uninformative, and the eventual resample collapses a wider cloud in one go. recovery_alpha_slow/fast stay at 0.001/0.1. Thrun recovery never fires on this route - injection draws uniformly from ~3074 m2 of free space, so an injected particle lands within 2.1 m of the converged cloud under 0.5% of the time, and across 984 cloud samples on three clean runs the farthest particle from its own cloud's median was 2.08 m. Disabling it would buy nothing and would cost the only automatic localization recovery the stack has: SetInitialPose re-seeds from the current estimate rather than correcting it, and nothing calls beluga's reinitialize_global_localization. REPRODUCING Evidence was gathered with fuse ON, which is NOT the default on main: use_fuse defaults to false in launch/sim/robot_drivers_to_persist_sim.launch.py, and with it off odom -> world is a static identity, odom -> base is simulator ground truth and AMCL has nothing to correct. Run with use_fuse:=true, or on top of the branch that flips that default, then drive "Navigate to Clicked Point" to (-8, 24) and "Navigate to Clicked Point with Replanning" to (-8, 20). test_amcl_scan_coverage.py ties max_beams to the dual_laser_merger's own parameters, since the two numbers live in different files with nothing connecting them and a mismatch is silent.
…dom origin This aimed a lot of effort at the wrong target, so it is worth writing down. map->odom's translation component is where the odom origin sits in the map, and an AMCL update is a rigid transform change, so any point is displaced by dt + (R_new - R_old)p. On hangar_sim's port-lane route the robot is up to 27 m from that origin, which turns a 0.008 rad yaw correction into a 0.16 m translation step and a 2 cm move of the robot. It also manufactures a false diagnostic clue: corrections look like they cluster in the featureless middle of the route and are absent for the first minute, which reads as a scan-structure problem. The lever arm grows monotonically along that same route and r(lever, step) is +0.42 to +0.52, so distance from spawn explains the pattern at least as well. Also records the two stack-lifecycle facts that cost box time today: there is no 'moveit_pro stop' (it is 'down --instance'), docker rm -f is undone by compose, and a second 'moveit_pro run' against a still-starting instance kills the runtime container with an endpoint reservation error that presents as a healthy drivers container with no /do_objective.
max_beams carried ~60 lines of justification, and there were further comment blocks on recovery_alpha_* and selective_resampling -- neither of which this branch changes. Explaining settings that were not touched is noise in a params file, and the measurements belong in the PR rather than beside the value.
It asserted only that nav2_params.yaml and localization_launch.py agree on a number; it never reads a scan, so it cannot prove the published sweep has that many rays -- the failure it was written to catch. It also bets on staying with beluga_amcl, which is an open question. The one-line comment beside max_beams carries the same information at no maintenance cost.
…ent gotchas Same two facts, a third the length, and the simulator teardown notes no longer sit under a heading about map jumping.
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Why
amcl.max_beamswas 60 and never moved when the lidar did./scan_mergednow publishes 723 rays.It is worse than 60-of-723, because beluga's
take_evenly()subsamples the raw ranges arraybefore invalid returns are filtered. Hangar scans are sparse — at the spawn pose, where
structure is best, 723 rays carry only 283 finite in-range returns, so
max_beams: 60kept~23 usable points. The filter was weighting 1000 particles on two dozen measurements, with no
error or warning. This claims density we already have.
What changed
max_beams60 → 725 (>= the ray count means "use every ray"), plus a one-line comment, and ashort
AGENTS.mdnote on measuring the correction at the robot rather than at the odom origin.Measured
Full
hangar_simstack, port-lane route, 60 vs 725 beams, one run each. Both SUCCEEDED.Largest correction at the robot: 0.046 m → 0.048 m. At the odom origin: 0.288 m → 0.198 m,
with the single step over 0.25 m gone.
CPU measured with the population pinned at the
max_particlesceiling of 5000 — the worst case,not the converged one: under ~2% of one core against a ~31% container baseline. Correction
interval and error against ground truth unchanged.
Two other settings were tried and left alone
selective_resampling(resample only once particle weights have spread, instead of everyupdate) — enabled, measured, reverted: it made corrections worse.
recovery_alpha_slow/fast(on low confidence, scatter particles across the whole map tore-localize) — considered disabling, left at stock: it never fires on this route, and it is
the only automatic localization recovery here.
Risk
Low. One parameter.