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

A Normal Distribution Transform-Based Radar Odometry Designed For Scanning and Automotive Radars