2019
DOI: 10.1109/access.2019.2898689
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Abstract: Road curb detection is essential for autonomous vehicles to locate themselves and make a rational decision, especially under road discontinuities, obstacle occlusions, and curved road scenarios. However, an effective and systematic solution to this problem has remained elusive. In this paper, a robust 3D-LiDAR-based method for road curb detection and tracking in a structured environment is proposed. The proposed method consists of four main stages: 1) a multi-feature based method is applied to extract candidat… Show more

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Cited by 52 publications
(34 citation statements)
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“…Another study was conducted on road edge detection of structured roads based on lidar alone. In [ 29 ], Wang et al proposed a method for road edge detection of structured roads based on robust 3D-lidar.…”
Section: Related Workmentioning
confidence: 99%
“…Another study was conducted on road edge detection of structured roads based on lidar alone. In [ 29 ], Wang et al proposed a method for road edge detection of structured roads based on robust 3D-lidar.…”
Section: Related Workmentioning
confidence: 99%
“…The change in the data position of the point cloud due to the movement of the vehicle affects the boundary extraction of the road. To solve this problem, we use the method of Wang G et al [6] to process the original point cloud data. This method obtains the posture and position of the mining truck at a certain moment by the position and posture between two adjacent frames.…”
Section: Data Preprocessingmentioning
confidence: 99%
“…The previous point has a larger impact on the actual detection of the road, thus the value of n is greater than m. Finally, the candidate points are fitted and filtered using the RANSAC algorithm. The least squares fitting is to fit all points including noise points [6]. RANSAC estimated polynomial model avoids noise fitting through multiple iterations.…”
Section: Boundary Points Fittingmentioning
confidence: 99%
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