  Existing radar sensors can be classified into automotive and scanning radars.
While most radar odometry (RO) methods are only designed for a specific type of
radar, our RO method adapts to both scanning and automotive radars. Our RO is
simple yet effective, where the pipeline consists of thresholding,
probabilistic submap building, and an NDT-based radar scan matching. The
proposed RO has been tested on two public radar datasets: the Oxford Radar
RobotCar dataset and the nuScenes dataset, which provide scanning and
automotive radar data respectively. The results show that our approach
surpasses state-of-the-art RO using either automotive or scanning radar by
reducing translational error by 51% and 30%, respectively, and rotational error
by 17% and 29%, respectively. Besides, we show that our RO achieves
centimeter-level accuracy as lidar odometry, and automotive and scanning RO
have similar accuracy.
