  We present an approach for radar-inertial odometry which uses a
continuous-time framework to fuse measurements from multiple automotive radars
and an inertial measurement unit (IMU). Adverse weather conditions do not have
a significant impact on the operating performance of radar sensors unlike that
of camera and LiDAR sensors. Radar's robustness in such conditions and the
increasing prevalence of radars on passenger vehicles motivate us to look at
the use of radar for ego-motion estimation. A continuous-time trajectory
representation is applied not only as a framework to enable heterogeneous and
asynchronous multi-sensor fusion, but also, to facilitate efficient
optimization by being able to compute poses and their derivatives in
closed-form and at any given time along the trajectory. We compare our
continuous-time estimates to those from a discrete-time radar-inertial odometry
approach and show that our continuous-time method outperforms the discrete-time
method. To the best of our knowledge, this is the first time a continuous-time
framework has been applied to radar-inertial odometry.
