| # Fixed-scene localization and navigation |
|
|
| The user explicitly authorized storing a map of the scene shared by all Task 01 |
| instances. This policy uses that known-scene condition. It does not assume that |
| the cans or bin remain in the same positions. |
|
|
| ## Offline preparation |
|
|
| `debug/build_scene_prior.py` fuses recorded RGB-D views using offline debug robot |
| poses as survey calibration. Debug task-object poses only mask cans and the bin |
| out of the survey. No task-object positions are exported. Static occupancy uses |
| surfaces above 0.38 m; localization references use textured floor and furniture. |
| The frozen atlas contains 57 views from 279 stable survey frames. Provenance is |
| in `assets/scene_prior_v1/survey_manifest.json`. |
|
|
| ## Runtime |
|
|
| 1. Extract ALIKED features from the official head RGB image and attach official |
| depth/calibration to each feature. |
| 2. Retrieve candidate reference views with a local visual-word histogram. |
| 3. Match ALIKED features with LightGlue and fit a metric rigid SE(2) transform |
| using RANSAC, vertical consistency, spatial support, and residual checks. |
| 4. Initialize only after strong geometric verification. Between absolute fixes, |
| use short RGB-D registrations and bounded proprioceptive velocity prediction. |
| 5. Route on the frozen occupancy map plus the current depth obstacle layer. The |
| live layer is rebuilt when observed, so localization corrections do not leave |
| duplicate walls in the static map. |
| 6. If tracking becomes stale, stop translation and search nearby viewing angles |
| before retrying a route. |
| 7. Prefer confirmed can proposals on unobstructed floor over furniture-overlap |
| proposals, but retain the latter as fallback candidates for real cans under |
| tables. Grasping and bin placement still refine geometry from live RGB-D. |
| 8. When furniture blocks the nominal close grasp stance, check hover, descent, |
| and lift IK from a farther collision-clear stance. The map clearance is not |
| reduced to force an approach. |
|
|
| No evaluation-time robot/world pose, object pose, instance ID, score, simulator |
| state file, backend request, or cloud model is passed to the policy. The frozen |
| prior and pretrained weights are explicit offline inputs. This is known-scene |
| evaluation, not a claim of unseen-scene generalization. |
|
|
| ## Algorithms and dependencies |
|
|
| - [LightGlue](https://github.com/cvg/LightGlue), upstream commit |
| `eb42fee2d71449efb0aa5c10549752b5d75384d8`, including ALIKED inference. |
| - [ALIKED](https://github.com/Shiaoming/ALIKED), `aliked-n16` weights. |
| - [Hierarchical Localization](https://github.com/cvg/Hierarchical-Localization): |
| reference retrieval followed by geometric localization informed the design; |
| the hloc package itself is not a runtime dependency. |
| - [RTAB-Map localization mode](https://github.com/introlab/rtabmap/wiki/FAQ): |
| fixed map with online pose correction informed the separation of mapping and |
| localization; RTAB-Map itself is not a runtime dependency. |
|
|
| Vendored LightGlue retains its upstream licenses. Two loading branches were |
| added to accept explicit local weight files; this adapter always uses those |
| branches. Kornia 0.8.3 and kornia_rs 0.1.14 are installed under |
| `third_party/python`, without changing the shared simulator environment. The |
| latter wheel is for Linux x86-64 / Python 3.11. Torch and torchvision come from |
| the existing environment. All weights are supplied under `assets/models`. |
|
|
| ## Validation |
|
|
| - Earlier replay after survey masking, before the repeated-floor rejection fix: 42/45 views localized; 37/45 met the |
| stricter 40-inlier initialization threshold. Successful matches had median |
| position error 3.23 mm, maximum 28.50 mm, and median localization time 34.4 ms. |
| Network weight loading was disabled during this replay. |
| - Instance 301 navigation-only probe: 905 steps and 0.23 m base travel, final position error 3.35 mm, |
| maximum 26.63 mm after localization. Estimates precede the action and debug |
| poses follow it, so the live comparison includes that one-step offset. |
| - Instance 301 completed a fresh-reset development episode at Q=1.0 in 5,745 steps, with no task recovery. This used structural-support and prediction-consistency gates to reject repeated-floor aliases found in the preceding trial. |
| - Current-gate replay on the floor-alias failure sequence: 29/35 views localized, maximum position error 28.52 mm, with no accepted metre-scale aliases. |
| - Complete frozen standalone evaluation: all 20 official instances, mean final Q **0.766667**, 13/20 official successes. See [EVALUATION.md](EVALUATION.md) for individual scores and remaining failures. The diagnostic localization errors above are not a measured bound for the full suite. |
|
|
| Use `--navigation prior` in `run.py` for this policy. Frozen runs copy/hash the |
| map, weights, dependencies, and source. The `visual` and `validated` modes remain |
| available for comparison. |
|
|
| Placement now checks a copied joint-state sequence including preparatory hand rotation, the original bounded extension, vertical descent, and retreat before moving the base. If depth stops a partial correction, it rechecks the observed current stance. A trial with accumulated segmented torso extension regressed and was removed; absolute joint bounds and map/depth clearances remain unchanged. |
|
|
