摘要
三维激光雷达被广泛应用在无人驾驶系统中对道路环境的检测和防碰撞检测。为增加激光雷达对扫描点云进行分割的准确性,提出一种基于深度投影的点云目标实时分割方法。首先采用体素化滤波去除噪声点,然后使用渐进式形态学滤波方法去除地面点,最后将点云进行深度投影,建立三维点云与深度投影图像的映射关系,利用深度投影图像的自适应角度阈值对点云进行目标分割,并将分割后的点云目标构造混合层次包围盒进行碰撞检测。实验结果表明该方法相对于传统聚类算法在时间效率上有明显提升,并且能有效降低过分割问题,实验数据目标分割准确率达到了78.82%,结合混合层次包围盒算法对分割后的点云目标进行碰撞检测,可有效地识别并判断物体空间位置关系,提升碰撞识别的准确性。
Three-dimensional LiDAR is widely used in unmanned driving systems,mainly to detect the road environment and for collision avoidance detection.A real-time method to segment the point cloud based on depth projection was proposed to increase the segmentation accuracy of a point cloud scanned by LiDAR.Voxel filtering was first used to remove noise points,after which progressive morphological filtering was used to remove ground points,and finally the point cloud was subjected to point depth projection.The adaptive angle threshold method for the depth projection image was used to segment the point cloud,and after segmentation of the point cloud target,a hybrid hierarchical bounding box was constructed for collision detection.The experimental results show that this method constitutes a significant improvement in time efficiency compared with traditional clustering algorithms,and can effectively reduce the problem of over-segmentation.The proposed method increased the segmentation accuracy rate in the experiment to 78.82%.The combined hierarchical bounding box algorithm is applied to the segmented points.
作者
王张飞
刘春阳
隋新
杨芳
马喜强
陈立海
WANG Zhang-fei;LIU Chun-yang;SUI Xin;YANG Fang;MA Xi-qiang;CHEN Li-hai(School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China;Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province, Luoyang 471003, China;Henan Key Laboratory for Machinery Design and Transmission System, Luoyang 471003, China)
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2020年第7期1600-1608,共9页
Optics and Precision Engineering
基金
国家自然科学基金资助项目(No.51805151)
河南省科技攻关计划(工业领域)资助项目(No.192102210141)
河南省高等学校重点科研项目基础研究计划资助项目(No.18A460018)
河南省高等学校青年骨干教师培养计划资助项目(No.2019GGJS082).
关键词
无人驾驶
激光雷达
点云目标分割
层次包围盒
三维碰撞检测
unmanned
LiDAR
point cloud target segmentation
hierarchical bounding box
3D collision detection
