Continuous-Time Radar-Inertial and Lidar-Inertial Odometry using a Gaussian Process Motion Prior
Abstract
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
Keywords
Cite
@article{arxiv.2402.06174,
title = {Continuous-Time Radar-Inertial and Lidar-Inertial Odometry using a Gaussian Process Motion Prior},
author = {Keenan Burnett and Angela P. Schoellig and Timothy D. Barfoot},
journal= {arXiv preprint arXiv:2402.06174},
year = {2024}
}
Comments
Accepted to IEEE Transactions on Robotics (2024-11-02)