Continuous-Time Radar-Inertial and Lidar-Inertial Odometry using a Gaussian Process Motion Prior
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| Format: | Preprint |
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2024
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| _version_ | 1866929598593236992 |
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| author | Burnett, Keenan Schoellig, Angela P. Barfoot, Timothy D. |
| author_facet | Burnett, Keenan Schoellig, Angela P. Barfoot, Timothy D. |
| contents | In this work, we demonstrate continuous-time radar-inertial and lidar-inertial odometry using a Gaussian process motion prior. Using a sparse prior, we demonstrate improved computational complexity during preintegration and interpolation. We use a white-noise-on-acceleration motion prior and treat the gyroscope as a direct measurement of the state while preintegrating accelerometer measurements to form relative velocity factors. Our odometry is implemented using sliding-window batch trajectory estimation. To our knowledge, our work is the first to demonstrate radar-inertial odometry with a spinning mechanical radar using both gyroscope and accelerometer measurements. We improve the performance of our radar odometry by \change{43\%} by incorporating an IMU. Our approach is efficient and we demonstrate real-time performance. Code for this paper can be found at: https://github.com/utiasASRL/steam_icp |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06174 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Continuous-Time Radar-Inertial and Lidar-Inertial Odometry using a Gaussian Process Motion Prior Burnett, Keenan Schoellig, Angela P. Barfoot, Timothy D. Robotics In this work, we demonstrate continuous-time radar-inertial and lidar-inertial odometry using a Gaussian process motion prior. Using a sparse prior, we demonstrate improved computational complexity during preintegration and interpolation. We use a white-noise-on-acceleration motion prior and treat the gyroscope as a direct measurement of the state while preintegrating accelerometer measurements to form relative velocity factors. Our odometry is implemented using sliding-window batch trajectory estimation. To our knowledge, our work is the first to demonstrate radar-inertial odometry with a spinning mechanical radar using both gyroscope and accelerometer measurements. We improve the performance of our radar odometry by \change{43\%} by incorporating an IMU. Our approach is efficient and we demonstrate real-time performance. Code for this paper can be found at: https://github.com/utiasASRL/steam_icp |
| title | Continuous-Time Radar-Inertial and Lidar-Inertial Odometry using a Gaussian Process Motion Prior |
| topic | Robotics |
| url | https://arxiv.org/abs/2402.06174 |