Abstract
In this paper, a tightly-coupled light detection and ranging (LiDAR)/dead reckoning (DR) navigation system with uncertain sampling time is designed for mobile robot localization. The Kalman filter (KF) is used as the main data fusion filter, where the state vector is composed of the position error, velocity error, yaw, and sampling time. The observation is provided of the difference between the LiDAR-derived and DR-derived distances measured from the corner feature points (CFPs) to the mobile robot. A real test experiment has been conducted to verify a good performance of the proposed method and show that it allows for a higher accuracy compared to the traditional LiDAR/DR integration.
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Acknowledgment
This work was supported by the Shandong Key R&D Program under Grants 2019GGXI04026 and 2019GNC106093.
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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Xu, Y., Shmaliy, Y.S., Shen, T., Bi, S., Guo, H. (2020). Improving Accuracy of Mobile Robot Localization by Tightly Fusing LiDAR and DR data. In: Zhang, YD., Wang, SH., Liu, S. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-030-51103-6_10
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DOI: https://doi.org/10.1007/978-3-030-51103-6_10
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