Neural Graph mAP: Dense Mapping with Efficient Loop Closure Integration

Abstract

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 novel RGB-D neural mapping framework in which the scene is represented by a collection of lightweight neural fields which are dynamically anchored to the pose graph of a sparse visual SLAM system. Our approach shows the ability to integrate large-scale loop closures while requiring only minimal reintegration. Furthermore we verify the scalability of our approach by demonstrating successful building-scale mapping taking multiple loop closures into account during the optimization and show that our method outperforms existing state-of-the-art approaches on large scenes in terms of quality and runtime. Our code is available open-source at https://github.com/KTH-RPL/neural_graph_mapping.