Abstract
On account of the low accuracy of boundary point cloud information during map construction of LiDAR used in mobile robots, an data processing scheme based on extended Kalman filter (EKF) and improved R-T-S smoothing and averaging is proposed to obtain accurate point cloud information. The proposed scheme can remove some noise points and make the map boundary more smoother and more accurate. The experimental results show that comparying with the original data, the proposed data processing scheme could reduce the position error of point cloud information effectively.
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Acknowledgment
This paper was supported by National Natural Science Foundation of China (No. 61803175), Shandong Provincial Natural Science Foundation (No. ZR2018LF01, No. ZR2020KF027), Shandong K&D Program (No. 2019GGX104026).
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Zhang, B., Wang, M., Bi, S., Li, F. (2021). LiDAR Map Construction Using Improved R-T-S Smoothing Assisted Extended Kalman Filter. In: Fu, W., Xu, Y., Wang, SH., Zhang, Y. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-030-82562-1_50
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DOI: https://doi.org/10.1007/978-3-030-82562-1_50
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