Neural Graph Mapping for Dense SLAM with Efficient Loop Closure
<|reference_start|>Neural Graph Mapping for Dense SLAM with Efficient Loop Closure: 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 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 at https://kth-rpl.github.io/neural_graph_mapping/.<|reference_end|>
@article{bruns2024neural,
    title={Neural Graph Mapping for Dense SLAM with Efficient Loop Closure},
    author={Leonard Bruns, Jun Zhang, Patric Jensfelt},
    journal={arXiv preprint arXiv:2405.03633},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03633},
    primaryClass={cs.CV cs.RO}
}