Object Persistence in SLAM Systems

Maintaining persistent object identities in SLAM systems is crucial for long-term autonomy and scene understanding. This article discusses our approach to object persistence across challenging scenarios.

The Challenge

Traditional SLAM systems struggle with:

• Re-identifying objects after long time gaps
• Handling significant viewpoint changes
• Managing objects with similar appearances
• Dealing with partial occlusions

Our Approach

We developed a multi-faceted solution combining:

1. Geometric Consistency

Using geometric constraints to verify object identity across frames, even when appearance changes.

2. Semantic Features

Leveraging high-level semantic features that remain consistent across viewpoints.

3. Temporal Tracking

Maintaining object histories to improve re-identification accuracy.

Implementation Details

Our system maintains a persistent object map with:

• 3D bounding boxes and poses
• Semantic labels and confidence scores
• Appearance descriptors (multi-view)
• Temporal association graph

Results

Testing on long-term indoor datasets showed:

• 87% re-identification accuracy after 24-hour gaps
• Robust performance across 180° viewpoint changes
• Improved loop closure detection by 34%

Future Work

We're exploring integration with foundation models for even more robust object understanding and persistence.