Towards Revisiting Visual Place Recognition for Joining Submaps in Multimap SLAM

2024-07-17

arXiv.org

AI Summary

In multi-map SLAM, submap disconnection due to robot localization failure impedes robust submap registration. To address this, we propose a cross-submap robust registration method leveraging visual place recognition (VPR) as a semantic bridge. Unlike conventional VPR-based loop closure detection, our approach elevates VPR to a structured geometric prior for submap association—enabling alignment across non-overlapping domains, disparate time periods, and heterogeneous sensor modalities. The method integrates deep feature matching, geometric consistency verification, and graph-based pose optimization, incorporating a lightweight VPR retriever and a differentiable pose solver. Evaluated on KITTI, NCLT, and a custom multi-submap dataset, our method achieves a 23.6% improvement in submap connection success rate, reduces absolute pose error to 0.48 m / 1.2°, and operates at 12 fps.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Problem

• Visual SLAM
• Map Mosaicing
• Robot Localization

Innovation

• Visual SLAM
• Puzzle Algorithm
• Time-Sequential Mapping