Continuous-Time Radar-Inertial and Lidar-Inertial Odometry using a Gaussian Process Motion Prior
<|reference_start|>Continuous-Time Radar-Inertial and Lidar-Inertial Odometry using a Gaussian Process Motion Prior: 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 19\% by incorporating an IMU. Our approach is efficient and we demonstrate real-time performance. Code for this project can be found at: https://github.com/utiasASRL/steam_icp<|reference_end|>
@article{burnett2024continuous-time,
    title={Continuous-Time Radar-Inertial and Lidar-Inertial Odometry using a
  Gaussian Process Motion Prior},
    author={Keenan Burnett, Angela P. Schoellig, Timothy D. Barfoot},
    journal={arXiv preprint arXiv:2402.06174},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06174},
    primaryClass={cs.RO}
}