Abstract
Point clouds from light detecting and ranging (LiDAR) sensors represent increasingly important information for environmental object detection and classification of automated and intelligent vehicles. Objects in the driving environment can be classified as either or depending on their movement characteristics. A LiDAR point cloud is also segmented into and points based on the motion properties of the measured objects. The segmented motion information of a point cloud can be useful for various functions in automated and intelligent vehicles. This paper presents a fast motion segmentation algorithm that segments a LiDAR point cloud into and points in real-time. The segmentation algorithm classifies the motion of the latest point cloud based on the LiDAR’s laser beam characteristics and the geometrical relationship between consecutive LiDAR point clouds. To accurately and reliably estimate the motion state of each LiDAR point considering the measurement uncertainty, both probability theory and evidence theory are employed in the segmentation algorithm. The probabilistic and evidential algorithm segments the point cloud into three classes: , , and . Points are placed in the class when LiDAR point cloud is not sufficient for motion segmentation. The point motion segmentation algorithm was evaluated quantitatively and qualitatively through experimental comparisons with previous motion segmentation methods.
Highlights
light detecting and ranging (LiDAR) systems are rapidly becoming an integral part of automated and intelligent vehicles for environmental awareness
This paper proposes an algorithm to rapidly segment the motion states of a point cloud detected by LiDAR in real-time
This paper focuses on the characteristics of lasers, such as multi-echo, beam divergence, and horizontal and vertical resolution, so that it can segment the motion of points more accurately than existing algorithms, such as occupancy grid mapping
Summary
LiDAR systems are rapidly becoming an integral part of automated and intelligent vehicles for environmental awareness. Points classified as static are measured from the surfaces of static objects, such as curbs, poles, buildings, and parked vehicles Such a static point cloud can be applied to various automated and intelligent driving functions, such as mapping, localization, and collision avoidance systems [3,4,5]. Points classified as dynamic are detected from objects that have speeds above a certain level, such as nearby moving vehicles, motorcycles, and pedestrians These points can be used for object tracking or motion prediction, which are necessary functions for automated and intelligent vehicles for tasks such as autonomous emergency braking (AEB), lane keeping, traffic jam assistance, and adaptive cruise control (ACC) systems. Because all the proposed updating processes are real-time, they are suitable for real-time application in automated and intelligent vehicle systems
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