Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Probabilistic Collision Detection for Non-Gaussian Noise
  Distributions   We present an efficient algorithm to compute tight upper bounds of collision
probability between two objects with positional uncertainties, whose error
distributions are represented with non-Gaussian forms. Our approach can handle
noisy datasets from depth sensors, whose distributions may correspond to
Truncated Gaussian, Weighted Samples, or Truncated Gaussian Mixture Model. We
derive tight probability bounds for convex shapes and extend them to non-convex
shapes using hierarchical representations. We highlight the benefits of our
approach over prior probabilistic collision detection algorithms in terms of
tighter bounds ($10$x) and improved running time ($3$x). Moreover, we use our
tight bounds to design an efficient and accurate motion planning algorithm for
a 7-DOF robot arm operating in tight scenarios with sensor and motion
uncertainties.

5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol