  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
