Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust Feature-Based Point Registration Using Directional Mixture Model   This paper presents a robust probabilistic point registration method for
estimating the rigid transformation (i.e. rotation matrix and translation
vector) between two pointcloud dataset. The method improves the robustness of
point registration and consequently the robot localization in the presence of
outliers in the pointclouds which always occurs due to occlusion, dynamic
objects, and sensor errors. The framework models the point registration task
based on directional statistics on a unit sphere. In particular, a Kent
distribution mixture model is adopted and the process of point registration has
been carried out in the two phases of Expectation-Maximization algorithm. The
proposed method has been evaluated on the pointcloud dataset from LiDAR sensors
in an indoor environment.

23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg