  This paper presents an accurate, highly efficient, and learning-free method
for large-scale odometry estimation using spinning radar, empirically found to
generalize well across very diverse environments -- outdoors, from urban to
woodland, and indoors in warehouses and mines - without changing parameters.
Our method integrates motion compensation within a sweep with one-to-many scan
registration that minimizes distances between nearby oriented surface points
and mitigates outliers with a robust loss function. Extending our previous
approach CFEAR, we present an in-depth investigation on a wider range of data
sets, quantifying the importance of filtering, resolution, registration cost
and loss functions, keyframe history, and motion compensation. We present a new
solving strategy and configuration that overcomes previous issues with sparsity
and bias, and improves our state-of-the-art by 38%, thus, surprisingly,
outperforming radar SLAM and approaching lidar SLAM. The most accurate
configuration achieves 1.09% error at 5Hz on the Oxford benchmark, and the
fastest achieves 1.79% error at 160Hz.
