融合激光雷达与视觉信息的小型障碍物测量方法

A Small Obstacle Measurement Method Based on Fusion of LIDAR and Visual Information

激光雷达相比于视觉传感器具有抗干扰能力强、测量精度高的优势。针对多线激光雷达距离小型障碍物较远时点云数据稀疏、难以进行精确测量的问题,将YOLO与HSV空间颜色直方图匹配结合进行远距离的目标检测,在机器人运动过程中,当检测区域内的激光数据量满足要求时,根据传感器标定结果对此时的激光雷达数据进行聚类、特征点提取与关键参量计算,完成对障碍物的测量。使用16线Velodyne激光雷达与工业IDS相机进行方法验证,结果表明,该方法可提高激光雷达数据量7.83倍,即使是运动场景下也能保证测量小型障碍物的最大宽度误差小于2.4%,测距误差小于0.15%。

Compared with visual sensors,LIDAR has the advantages of strong anti-interference ability and high measurement accuracy.But it is difficult to make accurate measurements on the point cloud data which is sparse when multi-line LIDAR is far away from small obstacles.To solve the problem,YOLO(You Only Look Once)and HSV spatial color histogram matching are combined to perform long-range target detection.In the process of robot motion,when the amount of laser data in the detection area meets the requirements,the LIDAR data at this time is clustered,the feature points are extracted,and the key parameters are calculated based on the sensor calibration results,so as to complete the measurement of obstacles.A 16-line Velodyne LIDAR and an industrial IDS camera are used for verification of the method.The results show that this method can increase the data volume of LIDAR by up to 7.83 times.Even in motion scenes,it is guaranteed that the maximum width error of small obstacles is less than 2.4%,and the distance-measuring error is less than 0.15%.