Eigen Is All You Need: Efficient Lidar-Inertial Continuous-Time Odometry with Internal Association
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866914828828803072 |
|---|---|
| author | Nguyen, Thien-Minh Xu, Xinhang Jin, Tongxing Yang, Yizhuo Li, Jianping Yuan, Shenghai Xie, Lihua |
| author_facet | Nguyen, Thien-Minh Xu, Xinhang Jin, Tongxing Yang, Yizhuo Li, Jianping Yuan, Shenghai Xie, Lihua |
| contents | In this paper, we propose a continuous-time lidar-inertial odometry (CT-LIO) system named SLICT2, which promotes two main insights. One, contrary to conventional wisdom, CT-LIO algorithm can be optimized by linear solvers in only a few iterations, which is more efficient than commonly used nonlinear solvers. Two, CT-LIO benefits more from the correct association than the number of iterations. Based on these ideas, we implement our method with a customized solver where the feature association process is performed immediately after each incremental step, and the solution can converge within a few iterations. Our implementation can achieve real-time performance with a high density of control points while yielding competitive performance in highly dynamical motion scenarios. We demonstrate the advantages of our method by comparing with other existing state-of-the-art CT-LIO methods. The source code will be released for the benefit of the community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02337 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Eigen Is All You Need: Efficient Lidar-Inertial Continuous-Time Odometry with Internal Association Nguyen, Thien-Minh Xu, Xinhang Jin, Tongxing Yang, Yizhuo Li, Jianping Yuan, Shenghai Xie, Lihua Robotics In this paper, we propose a continuous-time lidar-inertial odometry (CT-LIO) system named SLICT2, which promotes two main insights. One, contrary to conventional wisdom, CT-LIO algorithm can be optimized by linear solvers in only a few iterations, which is more efficient than commonly used nonlinear solvers. Two, CT-LIO benefits more from the correct association than the number of iterations. Based on these ideas, we implement our method with a customized solver where the feature association process is performed immediately after each incremental step, and the solution can converge within a few iterations. Our implementation can achieve real-time performance with a high density of control points while yielding competitive performance in highly dynamical motion scenarios. We demonstrate the advantages of our method by comparing with other existing state-of-the-art CT-LIO methods. The source code will be released for the benefit of the community. |
| title | Eigen Is All You Need: Efficient Lidar-Inertial Continuous-Time Odometry with Internal Association |
| topic | Robotics |
| url | https://arxiv.org/abs/2402.02337 |