Neural Graph Mapping for Dense SLAM with Efficient Loop Closure

arXiv:2405.03633v1 Announce Type: new
Abstract: Existing neural field-based SLAM methods typically employ a single monolithic field as their scene representation. This prevents efficient incorporation of loop closure constraints and limits scalability. To address these shortcomings, we propose a neural mapping framework which anchors lightweight neural fields to the pose graph of a sparse visual SLAM system. Our approach shows the ability to integrate large-scale loop closures, while limiting necessary reintegration. Furthermore, we verify the scalability of our approach by demonstrating …

arxiv cs.cv cs.ro graph loop mapping slam type