  This paper presents the accurate, highly efficient, and learning-free method
CFEAR Radarodometry for large-scale radar odometry estimation. By using a
filtering technique that keeps the k strongest returns per azimuth and by
additionally filtering the radar data in Cartesian space, we are able to
compute a sparse set of oriented surface points for efficient and accurate scan
matching. Registration is carried out by minimizing a point-to-line metric and
robustness to outliers is achieved using a Huber loss. We were able to
additionally reduce drift by jointly registering the latest scan to a history
of keyframes and found that our odometry method generalizes to different sensor
models and datasets without changing a single parameter. We evaluate our method
in three widely different environments and demonstrate an improvement over
spatially cross-validated state-of-the-art with an overall translation error of
1.76% in a public urban radar odometry benchmark, running at 55Hz merely on a
single laptop CPU thread.
