Geometric Multi-Session Map Merging with Learned Descriptors
Published:Dec 30, 2025 17:56
•1 min read
•ArXiv
Analysis
This paper addresses the important problem of merging point cloud maps from multiple sessions for autonomous systems operating in large environments. The use of learned local descriptors, a keypoint-aware encoder, and a geometric transformer suggests a novel approach to loop closure detection and relative pose estimation, crucial for accurate map merging. The inclusion of inter-session scan matching cost factors in factor-graph optimization further enhances global consistency. The evaluation on public and self-collected datasets indicates the potential for robust and accurate map merging, which is a significant contribution to the field of robotics and autonomous navigation.
Key Takeaways
- •Proposes a learning-based framework (GMLD) for multi-session point cloud map merging.
- •Employs a keypoint-aware encoder and plane-based geometric transformer for feature extraction.
- •Integrates inter-session scan matching cost factors for improved global consistency.
- •Demonstrates accurate and robust map merging with low error on various datasets.
Reference
“The results show accurate and robust map merging with low error, and the learned features deliver strong performance in both loop closure detection and relative pose estimation.”