摘要
本文提出一种基于3D视觉传感器的障碍物检测方法,用于解决准静态场景的障碍物检测的误检、漏检等问题。本文算法首先对3D视觉传感器得到的点云数据进行坐标系转换,然后以高度值作为尺度因子产生深度图像,然后与障碍物高度参数进行比较产生二值化图像,通过计算二值化图像最大连通域大小辨识出障碍物。本算法使用在变电站电力巡检机器人安全巡检项目中,通过长时间复杂环境运行验证了算法的稳定性和有效性。
This paper proposes a method of obstacle detection based on 3 D vision sensor,which is used to avoid false detection and missed detection of obstacle for robot navigation in quasi-static scenes.The algorithm firstly performed coordinate system conversion on the point cloud data obtained by the 3 D vision sensor.Secondly,the depth image was generated with the height value as the scale factor,and then it was compared with the obstacle height parameter to generate the binarized image.Finally,we calculates the maximum connectivtity of the binarized image,and the size of field identifies the obstacle.The algorithm is used in the substation power inspection robot safety inspection project to verify the stability and effectiveness of the algorithm through long-term complex environment operation.
作者
宋弦
陈锦龙
陈俊全
叶航超
Song Xuan;Chen Jinlong;Chen Junquan;Ye Hangchao(Guizhou Power Grid Co.,Ltd.,Guiyang,Guizhou550002,China)
出处
《应用激光》
CSCD
北大核心
2020年第6期1115-1119,共5页
Applied Laser
关键词
障碍物检测
导航
机器人
obstacle detection
navigation
robot
