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Journal Article FusionLoc: Camera-2D LiDAR Fusion Using Multi-Head Self-Attention for End-to-End Serving Robot Relocalization
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Authors
Jieun Lee, Hakjun Lee, Jiyong Oh
Issue Date
2023-07
Citation
IEEE Access, v.11, pp.75121-75133
ISSN
2169-3536
Publisher
Institute of Electrical and Electronics Engineers Inc.
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1109/ACCESS.2023.3297202
Abstract
As technology advances in autonomous mobile robots, mobile service robots have been actively used more and more for various purposes. Especially, serving robots have been not surprising products anymore since the COVID-19 pandemic. One of the practical problems in operating a serving robot is that it often fails to estimate its pose on a map that it moves around. Whenever the failure happens, servers should bring the serving robot to its initial location and reboot it manually. In this paper, we focus on end-to-end relocalization of serving robots to address the problem. It is to predict robot pose directly from only the onboard sensor data using neural networks. In particular, we propose a deep neural network architecture for the relocalization based on camera-2D LiDAR sensor fusion. We call the proposed method FusionLoc. In the proposed method, the multi-head self-attention complements different types of information captured by the two sensors to regress the robot pose. Our experiments on a dataset collected by a commercial serving robot demonstrate that FusionLoc can provide better performances than previous end-to-end relocalization methods taking only a single image or a 2D LiDAR point cloud as well as a straightforward fusion method concatenating their features.
KSP Keywords
2D LiDAR, Autonomous mobile robots, Deep neural network(DNN), End to End(E2E), Fusion method, LIDAR point cloud, LiDAR sensors, Mobile services, Multi-head, Robot relocalization, Service robots
This work is distributed under the term of Creative Commons License (CCL)
(CC BY NC ND)
CC BY NC ND