Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semantic Nearest Neighbor Fields Monocular Edge Visual-Odometry   Recent advances in deep learning for edge detection and segmentation opens up
a new path for semantic-edge-based ego-motion estimation. In this work, we
propose a robust monocular visual odometry (VO) framework using category-aware
semantic edges. It can reconstruct large-scale semantic maps in challenging
outdoor environments. The core of our approach is a semantic nearest neighbor
field that facilitates a robust data association of edges across frames using
semantics. This significantly enlarges the convergence radius during tracking
phases. The proposed edge registration method can be easily integrated into
direct VO frameworks to estimate photometrically, geometrically, and
semantically consistent camera motions. Different types of edges are evaluated
and extensive experiments demonstrate that our proposed system outperforms
state-of-art indirect, direct, and semantic monocular VO systems.

16
The categories of this paper are: stat
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph