Continuous-Time Radar-Inertial and Lidar-Inertial Odometry Using a Gaussian Process Motion Prior

Keenan Burnett, Angela P. Schoellig, Timothy D. Barfoot

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 43% by incorporating an inertial measurement unit. Our approach is efficient and we demonstrate real-time performance. Code for this article can be found at: https://github.com/utiasASRL/steam_icp.