Scene Gaussian Map Alignment

Scene Gaussian Map Alignment

Use this when a bundle combines a 3D Gaussian/splat/PLY/USD/OBJ scene, a robot map such as Nav2 YAML plus occupancy PGM, semantic anchors from navigation_memory.json, or an Isaac/operator-console report that must label evidence honestly.

Boundary

Keep stable, repeatable work in scripts. The skill owns the judgment that changes from scene to scene:

• Stable scripts parse headers, bounds, map metadata, semantic-memory JSON, transforms, smoke artifacts, images, and HTML reports.
• The skill decides the evidence tier, asks for missing assets, chooses which anchors are credible, names blockers, and prevents overclaiming.
• Do not hide scene-specific assumptions in a script default. If an assumption will vary with the next Gaussian scene, write it in the skill/report as an explicit decision.

Evidence Tiers

Use these labels consistently:

• blocked: geometry, map files, semantic anchors, or coordinate evidence are missing.
• candidate: bbox fit, scale/translate, manual placement, or another heuristic alignment exists.
• verified: named physical/semantic anchors match across map and scene with residuals recorded.
• runtime_proven: Isaac or robot-view smoke renders/navigates through candidate waypoints and writes view evidence.
• planner_backed: a real planner/Nav2-equivalent path proof exists.

Never skip tiers in wording. A runtime smoke can prove that rendered robot views exist at candidate poses; it does not by itself prove Nav2 planner parity.

Workflow

1. Inventory every asset before aligning: Gaussian/splat/PLY files and whether they are rendered or only inspected, USD/OBJ/mesh world bounds, Nav2 YAML, occupancy grid, semantic memory, map-bundle context, anchor ids, and any segmentation/object manifest/correspondence/calibration evidence.

2. Run the deterministic tools that apply to the available assets:

   python -m roboclaws.household.agibot_map_bundle \
     --source-map-dir <map-root> \
     --context-json <context-json> \
     --output-dir assets/maps/<bundle-name>
   
   .venv-isaaclab/bin/python -m roboclaws.backends.isaaclab.b1_readiness \
     --b1-root <scene-root> \
     --map12-root <map-root> \
     --output output/<run>/readiness.json
   
   .venv-isaaclab/bin/python -m roboclaws.backends.isaaclab.b1_navigation_smoke \
     --b1-root <scene-root> \
     --map12-root <map-root> \
     --output-dir output/<run> \
     --accept-nvidia-eula
   
   .venv-isaaclab/bin/python -m roboclaws.backends.isaaclab.b1_readiness \
     --b1-root <scene-root> \
     --map12-root <map-root> \
     --navigation-artifact output/<run>/navigation_smoke.json \
     --require-navigation-success \
     --output output/<run>/readiness_with_navigation.json
   
   python -m roboclaws.backends.isaaclab.b1_navigation_report \
     --run-dir output/<run>
   

3. Classify what the run actually proved: bbox fit is candidate; matched anchors with residuals are verified; rendered robot views at candidate poses are runtime_proven; planner path evidence is planner_backed.

4. Summarize the evidence without changing the artifacts:

   python skills/scene-gaussian-map-alignment/scripts/summarize_alignment_evidence.py \
     --readiness-artifact output/<run>/readiness_with_navigation.json \
     --navigation-artifact output/<run>/navigation_smoke.json \
     --output output/<run>/alignment_evidence_summary.json
   

5. Write the lightweight alignment manifest. This is the fusion contract for future runs; it is not a fused USD/Gaussian scene:

   python skills/scene-gaussian-map-alignment/scripts/summarize_alignment_evidence.py \
     manifest \
     --readiness-artifact output/<run>/readiness_with_navigation.json \
     --navigation-artifact output/<run>/navigation_smoke.json \
     --evidence-summary output/<run>/alignment_evidence_summary.json \
     --map-bundle assets/maps/<bundle-name> \
     --output output/<run>/alignment_manifest.json
   

6. Report the open blockers and next promotion step. Prefer one precise blocker over broad language like "alignment done".

Honest Labels

• Do not claim Gaussian fusion unless the renderer consumed the Gaussian/splat asset; header/bounds inspection is only inventory evidence.
• Do not claim semantic_anchors_are_usd_truth=true without segmentation, object manifest, or anchor correspondences that bind map anchors to USD/scene objects.
• Do not claim manipulation support without object/receptacle binding plus a pick/place proof.
• A "verify image" is a rendered camera view from a candidate pose. It helps inspect gross placement and visibility, but it is not ground-truth alignment, semantic binding, or planner proof.
• If Map 12 semantics are used only as navigation-memory anchors, call them robot_map_12_navigation_memory_overlay or an equivalent overlay source, not USD truth.

Output

When handing off results, include:

• alignment tier and transform source;
• whether Gaussian assets were rendered or only inspected;
• semantic source and semantic/USD binding status;
• navigation evidence status and whether it is planner-backed;
• artifact paths such as readiness.json, navigation_smoke.json, readiness_with_navigation.json, alignment_evidence_summary.json, alignment_manifest.json, report.html, and any map bundle;
• the exact next step needed to promote the evidence tier.

Acceptance

After changing this skill, related scripts, or map/Isaac report contracts, run:

./scripts/dev/run_pytest_standalone.sh \
  tests/contract/maps/test_b1_map12_digital_twin_readiness.py \
  tests/contract/maps/test_b1_map12_navigation_report.py \
  tests/contract/maps/test_agibot_map_bundle_export.py \
  tests/contract/skills/test_scene_gaussian_map_alignment_skill.py \
  tests/contract/skills/test_skill_manifests.py \
  -q