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首页> 外文期刊>Optical engineering >Obstacle information detection method based on multiframe three-dimensional lidar point cloud fusion
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Obstacle information detection method based on multiframe three-dimensional lidar point cloud fusion

机译：基于多帧三维激光雷达点云融合的障碍物信息检测方法

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摘要

An obstacle detection method based on multiframe point cloud fusion and ground plane estimation is proposed. First, the robot, three-dimensional lidar, and two-axis rotating platform motion models were established. With the pitch rotation of lidar and the movement of the robot, the multiframe point cloud data were superposed to obtain a dense point cloud. Then, the incremental linear fitting method was adopted to estimate the ground plane to eliminate the ground disturbance. Finally, the pairwise linkage clustering method could cluster multiple obstacles and the projection method could obtain the specific size information of obstacles. Therefore, verification of experiments was carried on the Robotics Lab Platform (BIT-NAZA), experimental results showed that multiframe point cloud data can be accurately integrated to obtain dense point cloud and then get information of obstacles. For some low-height obstacles with sparse density of point clouds, the detection accuracy of an object with a height of about 10 cm is about 95%, whereas for other higher objects, the detection rate will be higher. So the proposed algorithm can achieve its detection and related parameter acquisition efficiently.
机译：提出了一种基于多帧点云融合和地平面估计的障碍物检测方法。首先，建立了机器人，三维激光雷达和两轴旋转平台运动模型。随着激光雷达的俯仰旋转和机器人的移动，多帧点云数据被叠加以获得密集点云。然后，采用增量线性拟合法对地平面进行估计，以消除地面干扰。最后，成对链接聚类方法可以对多个障碍物进行聚类，投影方法可以获得障碍物的具体尺寸信息。因此，在机器人实验室平台（BIT-NAZA）上进行了实验验证，实验结果表明，可以准确地集成多帧点云数据以获得密集点云，然后获取障碍物信息。对于一些点云稀疏的低高度障碍物，高约10 cm的物体的检测精度约为95％，而对于其他较高的物体，其检测率将更高。因此，该算法可以有效地实现其检测和相关参数获取。

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