Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: RadarLoc: Learning to Relocalize in FMCW Radar   Relocalization is a fundamental task in the field of robotics and computer
vision. There is considerable work in the field of deep camera relocalization,
which directly estimates poses from raw images. However, learning-based methods
have not yet been applied to the radar sensory data. In this work, we
investigate how to exploit deep learning to predict global poses from Emerging
Frequency-Modulated Continuous Wave (FMCW) radar scans. Specifically, we
propose a novel end-to-end neural network with self-attention, termed RadarLoc,
which is able to estimate 6-DoF global poses directly. We also propose to
improve the localization performance by utilizing geometric constraints between
radar scans. We validate our approach on the recently released challenging
outdoor dataset Oxford Radar RobotCar. Comprehensive experiments demonstrate
that the proposed method outperforms radar-based localization and deep camera
relocalization methods by a significant margin.

15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
18
The categories of this paper are: econ
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th