FusedNet: End-to-End Mobile Robot Relocalization in Dynamic Large-Scale Scene

To improve robot relocalization accuracy in both static and dynamic environments, we introduce a novel network, FusedNet, which incorporates a cross-attention to fuse global and local image features for end-to-end relocalization. This approach relies solely on a monocular camera sensor that is fixed...

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Bibliographic Details
Published inIEEE robotics and automation letters Vol. 9; no. 5; pp. 4099 - 4105
Main Authors Chen, Fang-xing, Tang, Yifan, Tai, Cong, Liu, Xue-ping, Wu, Xiang, Zhang, Tao, Zeng, Long
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.05.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
3-DOF
Cameras
cross-attention
end-to-end relocalization
Feature extraction
fused feature
Localization
Mobile robot
Mobile robots
Point cloud compression
Robot sensing systems
Robots
Task analysis
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Summary:To improve robot relocalization accuracy in both static and dynamic environments, we introduce a novel network, FusedNet, which incorporates a cross-attention to fuse global and local image features for end-to-end relocalization. This approach relies solely on a monocular camera sensor that is fixed on the mobile robot, and directly predicts the absolute pose from the input RGB image. Additionally, we have collected a mobile robot relocalization dataset, termed moBotReloc, consisting of dynamic large-scale scenes, using the Unity 3D simulation platform and a real mobile robot. Through extensive experiments on 7Scenes and moBotReloc, we demonstrate that FusedNet achieves significant accuracy in 6-DoF camera relocalization in static scenes, and exhibits superior relocalization performance in dynamic large-scale scenes for mobile robot applications, outperforming existing end-to-end methods that rely solely on a single global or local feature.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2024.3372465