Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Good Feature Selection for Least Squares Pose Optimization in VO/VSLAM   This paper aims to select features that contribute most to the pose
estimation in VO/VSLAM. Unlike existing feature selection works that are
focused on efficiency only, our method significantly improves the accuracy of
pose tracking, while introducing little overhead. By studying the impact of
feature selection towards least squares pose optimization, we demonstrate the
applicability of improving accuracy via good feature selection. To that end, we
introduce the Max-logDet metric to guide the feature selection, which is
connected to the conditioning of least squares pose optimization problem. We
then describe an efficient algorithm for approximately solving the NP-hard
Max-logDet problem. Integrating Max-logDet feature selection into a
state-of-the-art visual SLAM system leads to accuracy improvements with low
overhead, as demonstrated via evaluation on a public benchmark.

25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat