Deep Sensor Fusion between 2D Laser Scanner and IMU for Mobile Robot Localization

Chi Li; Sen Wang; Yan Zhuang; Fei Yan

doi:10.1109/JSEN.2019.2910826

Deep Sensor Fusion between 2D Laser Scanner and IMU for Mobile Robot Localization

Chi Li, Sen Wang, Yan Zhuang, Fei Yan

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

164 Downloads (Pure)

Abstract

Multi-sensor fusion plays a key role in 2D laser-based robot location and navigation. Although it has achieved great success, there are still some challenges, e.g., being fragile when having a large angular rotation. In this paper, we present a deep learning-based approach to localizing a mobile robot using a 2D laser and an inertial measurement unit. A novel recurrent convolutional neural network (RCNN)-based architecture is developed to fuse laser and inertial data for scan-to-scan pose estimation. A scan-to-submap optimization is also introduced to optimize the poses estimated by the RCNN for enhanced robustness and accuracy. Extensive experiments have been conducted in both simulation and practice with a real mobile robot, verifying the effectiveness of the proposed deep sensor fusion system.

Original language	English
Pages (from-to)	8501-8509
Number of pages	9
Journal	IEEE Sensors Journal
Volume	21
Issue number	6
Early online date	12 Apr 2019
DOIs	https://doi.org/10.1109/JSEN.2019.2910826
Publication status	Published - 15 Mar 2021

Keywords

2D laser scanning
Data fusion
inertial measurement unit (IMU)
pose estimation

ASJC Scopus subject areas

Instrumentation
Electrical and Electronic Engineering

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10.1109/JSEN.2019.2910826

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title = "Deep Sensor Fusion between 2D Laser Scanner and IMU for Mobile Robot Localization",

abstract = "Multi-sensor fusion plays a key role in 2D laser-based robot location and navigation. Although it has achieved great success, there are still some challenges, e.g., being fragile when having a large angular rotation. In this paper, we present a deep learning-based approach to localizing a mobile robot using a 2D laser and an inertial measurement unit. A novel recurrent convolutional neural network (RCNN)-based architecture is developed to fuse laser and inertial data for scan-to-scan pose estimation. A scan-to-submap optimization is also introduced to optimize the poses estimated by the RCNN for enhanced robustness and accuracy. Extensive experiments have been conducted in both simulation and practice with a real mobile robot, verifying the effectiveness of the proposed deep sensor fusion system.",

keywords = "2D laser scanning, Data fusion, inertial measurement unit (IMU), pose estimation",

author = "Chi Li and Sen Wang and Yan Zhuang and Fei Yan",

note = "Funding Information: Manuscript received January 30, 2019; revised April 4, 2019; accepted April 6, 2019. Date of publication April 12, 2019; date of current version February 17, 2021. This work was supported by the National Natural Science Foundation of China under Grant 61503056 and Grant U1508208. The associate editor coordinating the review of this article and approving it for publication was Prof. Subhas C. Mukhopadhyay. (Corresponding author: Yan Zhuang.) C. Li, Y. Zhuang, and F. Yan are with the School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China (e-mail: lichiduter@mail.dlut.edu.cn; zhuang@dlut.edu.cn; fyan@dlut.edu.cn). Publisher Copyright: {\textcopyright} 2001-2012 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",

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N1 - Funding Information: Manuscript received January 30, 2019; revised April 4, 2019; accepted April 6, 2019. Date of publication April 12, 2019; date of current version February 17, 2021. This work was supported by the National Natural Science Foundation of China under Grant 61503056 and Grant U1508208. The associate editor coordinating the review of this article and approving it for publication was Prof. Subhas C. Mukhopadhyay. (Corresponding author: Yan Zhuang.) C. Li, Y. Zhuang, and F. Yan are with the School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China (e-mail: lichiduter@mail.dlut.edu.cn; zhuang@dlut.edu.cn; fyan@dlut.edu.cn). Publisher Copyright: © 2001-2012 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/3/15

Y1 - 2021/3/15

N2 - Multi-sensor fusion plays a key role in 2D laser-based robot location and navigation. Although it has achieved great success, there are still some challenges, e.g., being fragile when having a large angular rotation. In this paper, we present a deep learning-based approach to localizing a mobile robot using a 2D laser and an inertial measurement unit. A novel recurrent convolutional neural network (RCNN)-based architecture is developed to fuse laser and inertial data for scan-to-scan pose estimation. A scan-to-submap optimization is also introduced to optimize the poses estimated by the RCNN for enhanced robustness and accuracy. Extensive experiments have been conducted in both simulation and practice with a real mobile robot, verifying the effectiveness of the proposed deep sensor fusion system.

AB - Multi-sensor fusion plays a key role in 2D laser-based robot location and navigation. Although it has achieved great success, there are still some challenges, e.g., being fragile when having a large angular rotation. In this paper, we present a deep learning-based approach to localizing a mobile robot using a 2D laser and an inertial measurement unit. A novel recurrent convolutional neural network (RCNN)-based architecture is developed to fuse laser and inertial data for scan-to-scan pose estimation. A scan-to-submap optimization is also introduced to optimize the poses estimated by the RCNN for enhanced robustness and accuracy. Extensive experiments have been conducted in both simulation and practice with a real mobile robot, verifying the effectiveness of the proposed deep sensor fusion system.

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Deep Sensor Fusion between 2D Laser Scanner and IMU for Mobile Robot Localization

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Sen Wang

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Deep Sensor Fusion between 2D Laser Scanner and IMU for Mobile Robot Localization

Abstract

Keywords

ASJC Scopus subject areas

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Sen Wang

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