Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Topometric Localization with Deep Learning   Compared to LiDAR-based localization methods, which provide high accuracy but
rely on expensive sensors, visual localization approaches only require a camera
and thus are more cost-effective while their accuracy and reliability typically
is inferior to LiDAR-based methods. In this work, we propose a vision-based
localization approach that learns from LiDAR-based localization methods by
using their output as training data, thus combining a cheap, passive sensor
with an accuracy that is on-par with LiDAR-based localization. The approach
consists of two deep networks trained on visual odometry and topological
localization, respectively, and a successive optimization to combine the
predictions of these two networks. We evaluate the approach on a new
challenging pedestrian-based dataset captured over the course of six months in
varying weather conditions with a high degree of noise. The experiments
demonstrate that the localization errors are up to 10 times smaller than with
traditional vision-based localization methods.

17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph