Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CFEAR Radarodometry -- Conservative Filtering for Efficient and Accurate
  Radar Odometry   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.

24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th