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Electronics 2019, 8(1), 43; https://doi.org/10.3390/electronics8010043

A Robust Registration Method for Autonomous Driving Pose Estimation in Urban Dynamic Environment Using LiDAR

1
Department of Military Vehicles, Military Transportation University, Tianjin 300161, China
2
Department of Computer Engineering, University of Alcalá, 28801 Alcalá de Henares (Madrid), Spain
3
Suzhou Automotive Research Institute, Tsinghua University, Suzhou 215134, China
4
Laboratory for Industrial Metrology and Automation, College of Engineering, University of Texas, El Paso, TX 79968, USA
5
School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
*
Authors to whom correspondence should be addressed.
Received: 8 November 2018 / Revised: 15 December 2018 / Accepted: 18 December 2018 / Published: 1 January 2019
(This article belongs to the Special Issue Signal Processing and Analysis of Electrical Circuit)
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Abstract

The registration of point clouds in urban environments faces problems such as dynamic vehicles and pedestrians, changeable road environments, and GPS inaccuracies. The state-of-the-art methodologies have usually combined the dynamic object tracking and/or static feature extraction data into a point cloud towards the solution of these problems. However, there is the occurrence of minor initial position errors due to these methodologies. In this paper, the authors propose a fast and robust registration method that exhibits no need for the detection of any dynamic and/or static objects. This proposed methodology may be able to adapt to higher initial errors. The initial steps of this methodology involved the optimization of the object segmentation under the application of a series of constraints. Based on this algorithm, a novel multi-layer nested RANSAC algorithmic framework is proposed to iteratively update the registration results. The robustness and efficiency of this algorithm is demonstrated on several high dynamic scenes of both short and long time intervals with varying initial offsets. A LiDAR odometry experiment was performed on the KITTI data set and our extracted urban data-set with a high dynamic urban road, and the average of the horizontal position errors was compared to the distance traveled that resulted in 0.45% and 0.55% respectively. View Full-Text
Keywords: intelligent vehicles; LiDAR odometry; range sensing; simultaneous localization and mapping (SLAM) intelligent vehicles; LiDAR odometry; range sensing; simultaneous localization and mapping (SLAM)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Wang, R.; Xu, Y.; Sotelo, M.A.; Ma, Y.; Sarkodie-Gyan, T.; Li, Z.; Li, W. A Robust Registration Method for Autonomous Driving Pose Estimation in Urban Dynamic Environment Using LiDAR. Electronics 2019, 8, 43.

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