We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
A Robust Registration Method for Autonomous Driving Pose Estimation in Urban Dynamic Environment Using LiDAR.
- Authors
Wang, Rendong; Xu, Youchun; Sotelo, Miguel Angel; Ma, Yulin; Sarkodie-Gyan, Thompson; Li, Zhixiong; Li, Weihua
- 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.
- Subjects
DRIVERLESS cars; GLOBAL Positioning System; POINT cloud; INTELLIGENT transportation systems; LIDAR
- Publication
Electronics (2079-9292), 2019, Vol 8, Issue 1, p43
- ISSN
2079-9292
- Publication type
Article
- DOI
10.3390/electronics8010043