robotics
The Robot Map Can Forget a Chair That Moved
Point-level confidence updates remove stale geometry instead of letting an open-vocabulary spatial memory grow forever by accumulation.
Summary
Point-level confidence updates remove stale geometry instead of letting an open-vocabulary spatial memory grow forever by accumulation.
Object-centric semantic maps often append new points without revisiting old ones, allowing segmentation errors and geometry from moved objects to persist. This framework gives every object point a confidence score updated from multi-view temporal evidence. Consistent observations reinforce a point; conflicting masks, depth and visibility cues suppress it. Object tracking and re-identification preserve identity across relocation while the point cloud itself remains editable. The authors report accuracy gains of 3.46 percent on Replica and 2.55 percent on ScanNet, plus navigation and embodied-question-answering success rates of 82.4 and 76.7 percent. Those results depend on the tested datasets and perception stack and do not imply a complete or error-free map of arbitrary dynamic spaces.
Why it matters
Point-level confidence updates remove stale geometry instead of letting an open-vocabulary spatial memory grow forever by accumulation.
Limits and context
- Those results depend on the tested datasets and perception stack and do not imply a complete or error-free map of arbitrary dynamic spaces.
Key claims
Point-level confidence updates remove stale geometry instead of letting an open-vocabulary spatial memory grow forever by accumulation.
Qualification: Those results depend on the tested datasets and perception stack and do not imply a complete or error-free map of arbitrary dynamic spaces.
Evidence: source-2026-10-25-008
Sources
- arXiv preprint 2610.31476, original v1arXiv · primary research
Corrections
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