TransLoc3D : Point Cloud based Large-scale Place Recognition using Adaptive Receptive Fields
release_lr7mfl4ru5bjlg7xbdshkqpc2a
by
Tian-Xing Xu, Yuan-Chen Guo, Yu-Kun Lai, Song-Hai Zhang
2021
Abstract
Place recognition plays an essential role in the field of autonomous driving
and robot navigation. Although a number of point cloud based methods have been
proposed and achieved promising results, few of them take the size difference
of objects into consideration. For small objects like pedestrians and vehicles,
large receptive fields will capture unrelated information, while small
receptive fields would fail to encode complete geometric information for large
objects such as buildings. We argue that fixed receptive fields are not well
suited for place recognition, and propose a novel Adaptive Receptive Field
Module (ARFM), which can adaptively adjust the size of the receptive field
based on the input point cloud. We also present a novel network architecture,
named TransLoc3D, to obtain discriminative global descriptors of point clouds
for the place recognition task. TransLoc3D consists of a 3D sparse
convolutional module, an ARFM module, an external transformer network which
aims to capture long range dependency and a NetVLAD layer. Experiments show
that our method outperforms prior state-of-the-art results, with an improvement
of 1.1\% on average recall@1 on the Oxford RobotCar dataset, and 0.8\% on the
B.D. dataset.
In text/plain
format
Archived Files and Locations
application/pdf 2.4 MB
file_absxvkb2zjcmtijx7lvoj4feru
|
arxiv.org (repository) web.archive.org (webarchive) |
2105.11605v1
access all versions, variants, and formats of this works (eg, pre-prints)