AC-LIO: Towards Asymptotic Compensation for Distortion in LiDAR-Inertial Odometry via Selective Intra-Frame Smoothing
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913848364105728 |
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| author | Zhang, Tianxiang Zhang, Xuanxuan Fan, Wenlei Xia, Xin Yu, Huai Wang, Lin Li, You |
| author_facet | Zhang, Tianxiang Zhang, Xuanxuan Fan, Wenlei Xia, Xin Yu, Huai Wang, Lin Li, You |
| contents | Existing LiDAR-Inertial Odometry (LIO) methods typically utilize the prior trajectory derived from the IMU integration to compensate for the motion distortion within LiDAR frames. However, discrepancies between the prior and true trajectory can lead to residual motion distortions that compromise the consistency of LiDAR frame with its corresponding geometric environment. This imbalance may result in pointcloud registration becoming trapped in local optima, thereby exacerbating drift during long-term and large-scale localization. To this end, we propose a novel LIO framework with selective intra-frame smoothing dubbed AC-LIO. Our core idea is to asymptotically backpropagate current update term and compensate for residual motion distortion under the guidance of convergence criteria, aiming to improve the accuracy of discrete-state LIO system with minimal computational increase. Extensive experiments demonstrate that our AC-LIO framework further enhances odometry accuracy compared to prior arts, with about 30.4% reduction in average RMSE over the second best result, leading to marked improvements in the accuracy of long-term and large-scale localization and mapping. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05873 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AC-LIO: Towards Asymptotic Compensation for Distortion in LiDAR-Inertial Odometry via Selective Intra-Frame Smoothing Zhang, Tianxiang Zhang, Xuanxuan Fan, Wenlei Xia, Xin Yu, Huai Wang, Lin Li, You Robotics Existing LiDAR-Inertial Odometry (LIO) methods typically utilize the prior trajectory derived from the IMU integration to compensate for the motion distortion within LiDAR frames. However, discrepancies between the prior and true trajectory can lead to residual motion distortions that compromise the consistency of LiDAR frame with its corresponding geometric environment. This imbalance may result in pointcloud registration becoming trapped in local optima, thereby exacerbating drift during long-term and large-scale localization. To this end, we propose a novel LIO framework with selective intra-frame smoothing dubbed AC-LIO. Our core idea is to asymptotically backpropagate current update term and compensate for residual motion distortion under the guidance of convergence criteria, aiming to improve the accuracy of discrete-state LIO system with minimal computational increase. Extensive experiments demonstrate that our AC-LIO framework further enhances odometry accuracy compared to prior arts, with about 30.4% reduction in average RMSE over the second best result, leading to marked improvements in the accuracy of long-term and large-scale localization and mapping. |
| title | AC-LIO: Towards Asymptotic Compensation for Distortion in LiDAR-Inertial Odometry via Selective Intra-Frame Smoothing |
| topic | Robotics |
| url | https://arxiv.org/abs/2412.05873 |