Tightly Coupled Range Inertial Odometry and Mapping with Exact Point Cloud Downsampling
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909598810636288 |
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| author | Koide, Kenji Takanose, Aoki Oishi, Shuji Yokozuka, Masashi |
| author_facet | Koide, Kenji Takanose, Aoki Oishi, Shuji Yokozuka, Masashi |
| contents | In this work, to facilitate the real-time processing of multi-scan registration error minimization on factor graphs, we devise a point cloud downsampling algorithm based on coreset extraction. This algorithm extracts a subset of the residuals of input points such that the subset yields exactly the same quadratic error function as that of the original set for a given pose. This enables a significant reduction in the number of residuals to be evaluated without approximation errors at the sampling point. Using this algorithm, we devise a complete SLAM framework that consists of odometry estimation based on sliding window optimization and global trajectory optimization based on registration error minimization over the entire map, both of which can run in real time on a standard CPU. The experimental results demonstrate that the proposed framework outperforms state-of-the-art CPU-based SLAM frameworks without the use of GPU acceleration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01017 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tightly Coupled Range Inertial Odometry and Mapping with Exact Point Cloud Downsampling Koide, Kenji Takanose, Aoki Oishi, Shuji Yokozuka, Masashi Robotics In this work, to facilitate the real-time processing of multi-scan registration error minimization on factor graphs, we devise a point cloud downsampling algorithm based on coreset extraction. This algorithm extracts a subset of the residuals of input points such that the subset yields exactly the same quadratic error function as that of the original set for a given pose. This enables a significant reduction in the number of residuals to be evaluated without approximation errors at the sampling point. Using this algorithm, we devise a complete SLAM framework that consists of odometry estimation based on sliding window optimization and global trajectory optimization based on registration error minimization over the entire map, both of which can run in real time on a standard CPU. The experimental results demonstrate that the proposed framework outperforms state-of-the-art CPU-based SLAM frameworks without the use of GPU acceleration. |
| title | Tightly Coupled Range Inertial Odometry and Mapping with Exact Point Cloud Downsampling |
| topic | Robotics |
| url | https://arxiv.org/abs/2505.01017 |