Neural Graph Map: Dense Mapping with Efficient Loop Closure Integration
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909659358560256 |
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| author | Bruns, Leonard Zhang, Jun Jensfelt, Patric |
| author_facet | Bruns, Leonard Zhang, Jun Jensfelt, Patric |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_03633 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Graph Map: Dense Mapping with Efficient Loop Closure Integration Bruns, Leonard Zhang, Jun Jensfelt, Patric Computer Vision and Pattern Recognition Robotics 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. |
| title | Neural Graph Map: Dense Mapping with Efficient Loop Closure Integration |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2405.03633 |