LiPO: LiDAR Inertial Odometry for ICP Comparison
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912067864231936 |
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| author | Mick, Darwin Pool, Taylor Nagaraju, Madankumar Sathenahally Kaess, Michael Choset, Howie Travers, Matt |
| author_facet | Mick, Darwin Pool, Taylor Nagaraju, Madankumar Sathenahally Kaess, Michael Choset, Howie Travers, Matt |
| contents | We introduce a LiDAR inertial odometry (LIO) framework, called LiPO, that enables direct comparisons of different iterative closest point (ICP) point cloud registration methods. The two common ICP methods we compare are point-to-point (P2P) and point-to-feature (P2F). In our experience, within the context of LIO, P2F-ICP results in less drift and improved mapping accuracy when robots move aggressively through challenging environments when compared to P2P-ICP. However, P2F-ICP methods require more hand-tuned hyper-parameters that make P2F-ICP less general across all environments and motions. In real-world field robotics applications where robots are used across different environments, more general P2P-ICP methods may be preferred despite increased drift. In this paper, we seek to better quantify the trade-off between P2P-ICP and P2F-ICP to help inform when each method should be used. To explore this trade-off, we use LiPO to directly compare ICP methods and test on relevant benchmark datasets as well as on our custom unpiloted ground vehicle (UGV). We find that overall, P2F-ICP has reduced drift and improved mapping accuracy, but, P2P-ICP is more consistent across all environments and motions with minimal drift increase. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_08097 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LiPO: LiDAR Inertial Odometry for ICP Comparison Mick, Darwin Pool, Taylor Nagaraju, Madankumar Sathenahally Kaess, Michael Choset, Howie Travers, Matt Robotics We introduce a LiDAR inertial odometry (LIO) framework, called LiPO, that enables direct comparisons of different iterative closest point (ICP) point cloud registration methods. The two common ICP methods we compare are point-to-point (P2P) and point-to-feature (P2F). In our experience, within the context of LIO, P2F-ICP results in less drift and improved mapping accuracy when robots move aggressively through challenging environments when compared to P2P-ICP. However, P2F-ICP methods require more hand-tuned hyper-parameters that make P2F-ICP less general across all environments and motions. In real-world field robotics applications where robots are used across different environments, more general P2P-ICP methods may be preferred despite increased drift. In this paper, we seek to better quantify the trade-off between P2P-ICP and P2F-ICP to help inform when each method should be used. To explore this trade-off, we use LiPO to directly compare ICP methods and test on relevant benchmark datasets as well as on our custom unpiloted ground vehicle (UGV). We find that overall, P2F-ICP has reduced drift and improved mapping accuracy, but, P2P-ICP is more consistent across all environments and motions with minimal drift increase. |
| title | LiPO: LiDAR Inertial Odometry for ICP Comparison |
| topic | Robotics |
| url | https://arxiv.org/abs/2410.08097 |