针对目前工业车辆测距避障技术中易受环境影响、信号干扰等问题, 提出一种基于双目定位测距的工业车辆实时避障方法. 首先, 对双目深度相机进行标定, 将采集到的车辆正后方作业环境图像进行双目立体矫正; 其次, 使用SGBM算法计算得到视差图, 并结合相机内参通过三角变换原理进行3D点云重建; 接下来, 对地面进行标定并拟合地面方程, 自定义有效检测范围及安全预警范围; 最后, 对行人进行方位检测, 设计了直行及转弯测距算法对范围内检测到的行人进行距离计算, 最终实现范围预警与实时规划避障. 4组实验结果表明, 行人测距算法在直行及转弯状态下0–3 m和3–5 m范围内误差均分别低于0.1 m和0.2 m, 行人检测算法的识别精度为97.38%, 检测帧率为22.12 fps, 该方法在设定范围内具有较高的灵敏性, 具备较好的实时避障效果.
A real-time obstacle avoidance method for industrial vehicles based on binocular positioning and ranging is proposed to solve the problems of environmental influence and signal interference faced by the current obstacle avoidance technology of industrial vehicles. Firstly, the binocular depth camera is calibrated, and binocular stereo correction is performed on the images of the operating environment directly behind the vehicle. Secondly, the SGBM algorithm is used to calculate the parallax map, and the 3D point cloud reconstruction is carried out by the trigonometric transformation principle in combination with internal parameters of the camera. Next, the ground calibration and ground equation fitting are conducted, and the effective detection range and safety warning range are defined. Finally, the orientation of pedestrians is detected, and the distance calculation of pedestrians detected in the range is carried out by the algorithm of straight and turn ranging, and the range warning and obstacle avoidance are realized in real time. Four groups of experiments show that the errors of the pedestrian ranging algorithm are lower than 0.1 m and 0.2 m in 0–3 m and 3–5 m in straight and turning states, respectively. The identification accuracy of the pedestrian detection algorithm is 97.38%, and the detection frame rate is 22.12 fps. The method has high sensitivity within the set range and good real-time obstacle avoidance effects.