作业环境的实时信息是工业机器人智能决策的重要依据，针对三维环境信息检 测的实时性和自适应性，文中提出了一种基于双目视觉的机器人作业环境( 目标体或障 碍物的形状、尺寸和位置) 检测方法． 首先，将 Canny 算子和 Otsu 算法相结合，采用降采样 和压缩梯度幅值级策略实现目标边缘的自动检测; 接着采用基于灰度相关的边缘点分类 匹配算法对边缘点进行分类和匹配，解决了传统立体匹配难以兼顾效率与精度的问题; 然 后基于点云数据环形排序数据结构型式，提出了基于边缘曲率角的轮廓三维几何及位置 信息的自动提取方法． 机器人自主作业中实时检测动态环境实验结果表明，文中提出的方 法能准确地获取作业环境中物体的三维信息，正确分辨出作业目标和障碍物，检测平面尺 寸平均误差为 0. 65%，高度误差为 1. 69%，机器人能依据目标位置和动态障碍物的实时 位置实现预期的作业任务． 

The realtime information of the working environment is the important basis for intelligent decision of robot． For the instantaneity and adaptivity of 3D environment information detection，a detection method of the shape， size and location of the object or obstacle based on binocular vision is proposed． Firstly，the Otsu into Canny is integrated． It improves the efficiency of the target edge by down sampling and compressing gradient magnitude level． Secondly，an edge-point classification matching algorithm based on gray correlation is applied to classify and match the edge point． In addition， it improves the efficiency and accuracy of the algorithm at the same time． Then，based on the structure of point clouds， the automatic extract method for 3D geometry and the location information of contours based on edge curvature angle are proposed． The experiments of the robot autonomous operation in dynamic environment show that the methods proposed are able to obtain the 3D information of the object in the working environment． The planar size error is 0. 65%，height error is 1. 69%， and distinguishes the object or obstacle accurately． The robot completes the expected task according to the realtime location information of the object and the obstacle．