Multiple Robots Localization Based on the Fusion of Ultra-Wideband Array and Odometry

LIU Ran; CAO Zhiqiang; DENG Tianrui; DENG Zhongyuan; XIAO Yufeng

doi:10.11999/JEIT210812

Volume 44 Issue 10

Oct. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(10): 3476-3484

LIU Ran, CAO Zhiqiang, DENG Tianrui, DENG Zhongyuan, XIAO Yufeng. Multiple Robots Localization Based on the Fusion of Ultra-Wideband Array and Odometry[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3476-3484. doi: 10.11999/JEIT210812

Citation:

LIU Ran, CAO Zhiqiang, DENG Tianrui, DENG Zhongyuan, XIAO Yufeng. Multiple Robots Localization Based on the Fusion of Ultra-Wideband Array and Odometry[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3476-3484. doi: 10.11999/JEIT210812

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Multiple Robots Localization Based on the Fusion of Ultra-Wideband Array and Odometry

doi: 10.11999/JEIT210812

School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China

Funds: The National Natural Science Foundation of China (12175187), The National Key Research Program (2019YFB1310805)

Received Date: 2021-08-10
Rev Recd Date: 2021-10-01

Available Online: 2021-11-19

Publish Date: 2022-10-19

Abstract

Abstract

An accurate relative localization is critical for multiple robots to realize collaboration and formation control. Visual or Light Detection And Ranging (LiDAR)-based approaches use feature matching to determine the relative pose between robots in indoor environments with Global Positioning System (GPS)-denied, but which is challenging in non-line-of-sight environment. To solve this problem, a relative positioning approach of mobile robots based on multiple Ultra WideBand (UWB) nodes is proposed. First, multiple UWB nodes carried by mobile robot are used to form UWB array, and the relative pose estimation between robots is realized through nonlinear optimization algorithm. To improve further the localization accuracy, the results of non-linear optimization are constrained through odometry measurements. In addition, in order to meet the real-time requirement, the relative pose and the odometry in the sliding window are optimized through the graph optimization algorithm. However, the uncertainty of the relative pose from the non-linear optimization is not known, thus it will affect the optimization accuracy. Therefore, this paper uses particle filtering to integrate the odometry and relative pose from sliding window to improve further the accuracy. The experimental results show that the proposed approach provides an average positioning error of 0.312 m and orientation error of 4.903° in an indoor environment with a size of 12×6 m, and has good real-time performance.
- Relative localization,
- Nonlinear optimization,
- Graph optimization,
- Sliding window,
- Particle filtering

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References(27)

References

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