  This article describes the method CFEAR Radar odometry, submitted to a
competition at the Radar in Robotics workshop, ICRA 20241. CFEAR is an
efficient and accurate method for spinning 2D radar odometry that generalizes
well across environments. This article presents an overview of the odometry
pipeline with new experiments on the public Boreas dataset. We show that a
real-time capable configuration of CFEAR - with its original parameter set -
yields surprisingly low drift in the Boreas dataset. Additionally, we discuss
an improved implementation and solving strategy that enables the most accurate
configuration to run in real-time with improved robustness, reaching as low as
0.61% translation drift at a frame rate of 68 Hz. A recent release of the
source code is available to the community
https://github.com/dan11003/CFEAR_Radarodometry_code_public, and we publish the
evaluation from this article on https://github.com/dan11003/cfear_2024_workshop
