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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.21293 |
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| _version_ | 1866915838707105792 |
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| author | Kurda, Aaron Steuernagel, Simon Baum, Marcus |
| author_facet | Kurda, Aaron Steuernagel, Simon Baum, Marcus |
| contents | Lidar-only odometry aims to estimate the trajectory of a mobile platform from a stream of lidar scans. Traditional scan-to map approaches register each scan against a single, evolving map, which propagates registration errors over time. To mitigate this, we propose a multitude-of-maps approach where the current scan is registered against multiple overlapping submaps instead of a single static map. By optimizing the resulting constraints in a pose graph, our method enables not only precise estimation of the current pose but also retrospective refinement of the submaps' anchor points, which improves short-term consistency and long-term accuracy. We demonstrate that our approach achieves competitive and often superior accuracy on a variety of automotive datasets while maintaining real-time performance. Ablation studies confirm the critical role of multiple registrations and retrospective refinement of the map as core factors for our accuracy gains. Code and raw results are available on our public GitHub at https://github.com/Fusion-Goettingen/IROS_2026_Kurda_Graph. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21293 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph-based Online Lidar Odometry with Retrospective Map Refinement Kurda, Aaron Steuernagel, Simon Baum, Marcus Robotics Lidar-only odometry aims to estimate the trajectory of a mobile platform from a stream of lidar scans. Traditional scan-to map approaches register each scan against a single, evolving map, which propagates registration errors over time. To mitigate this, we propose a multitude-of-maps approach where the current scan is registered against multiple overlapping submaps instead of a single static map. By optimizing the resulting constraints in a pose graph, our method enables not only precise estimation of the current pose but also retrospective refinement of the submaps' anchor points, which improves short-term consistency and long-term accuracy. We demonstrate that our approach achieves competitive and often superior accuracy on a variety of automotive datasets while maintaining real-time performance. Ablation studies confirm the critical role of multiple registrations and retrospective refinement of the map as core factors for our accuracy gains. Code and raw results are available on our public GitHub at https://github.com/Fusion-Goettingen/IROS_2026_Kurda_Graph. |
| title | Graph-based Online Lidar Odometry with Retrospective Map Refinement |
| topic | Robotics |
| url | https://arxiv.org/abs/2503.21293 |