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.

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

```bash python scripts/maps/exportagibotmap_bundle.py \ --source-map-dir \ --output-dir assets/maps/

.venv-isaaclab/bin/python scripts/isaaclabcleanup/checkb1map12_readiness.py \ --b1-root \ --map12-root \ --output output//readiness.json

.venv-isaaclab/bin/python scripts/isaaclabcleanup/runb1map12navigationsmoke.py \ --b1-root \ --map12-root \ --output-dir output/ \ --accept-nvidia-eula

.venv-isaaclab/bin/python scripts/isaaclabcleanup/checkb1map12readiness.py \ --b1-root \ --map12-root \ --navigation-artifact output//navigationsmoke.json \ --require-navigation-success \ --output output//readinesswithnavigation.json

python scripts/isaaclabcleanup/renderb1map12navigationreport.py \ --run-dir output/ ```

1. 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.

1. Summarize the evidence without changing the artifacts:

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

1. Write the lightweight alignment manifest. This is the fusion contract for

future runs; it is not a fused USD/Gaussian scene:

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

1. 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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: MiaoDX
• Source: MiaoDX/roboclaws
• License: MIT

Install and usage instructions live in the source repository linked above.