视觉引导下机器人拆垛场景识别定位抓取方法

Positioning and Grasping Method of Robot Destacking Scene Recognition Based on Vision Guidance

针对2D图像识别缺乏3D姿态信息,而传统的3D视觉需要处理大量点云,运算时间较长等问题,提出一种基于改进Mask R-CNN与局部点云迭代优化相结合的机器人拆垛、分拣及码垛策略。对Mask R-CNN网络进行改进,在其ROIAlign结构之后加入空间变换网络模块,提升识别准确率;利用改进的Mask R-CNN网络对目标进行实例分割,结合场景点云分割得到物体感兴趣区(ROI)场景局部点云;采用加入K维树邻域搜索的迭代最近点算法将物体ROI场景局部点云与模板点云进行配准,最终得到位姿估计的结果。UR5协作机器人根据此结果解决拆垛、分拣及码垛问题,实验结果表明:利用改进的Mask R-CNN网络提升了目标识别的准确率,使用ROI局部点云法减少了场景点云与模板点云配准的迭代次数,提高了工业机器人的拆垛、分拣及码垛效率。

In view of the lacking of 3D posture information in 2D image recognition, while a large number of point clouds need tobe dealt in traditional 3D vision and computing time is long, a robot destacking, sorting and palletizing strategy was proposed based on the combination of improved Mask R-CNN and local point cloud iterative optimization. The Mask R-CNN network was improved, and the spatial transformer networks(STN)module was added after the ROIAlign structure to improve the recognition accuracy.The improved Mask R-CNN network was used to segment instance, and it was combined with the scene point cloud to obtain the object region of interest(ROI) scene local point cloud. The interactive closest point(ICP) algorithm with K-dimensional tree neighborhood search was used to register the local point cloud of the object ROI scene with the template point cloud. Finally, the pose estimation result was obtained. UR5 cooperative robot solved the automatic destacking, sorting and palletizing problems according to the results.The experimental results show that using the improved Mask R-CNN network improves the accuracy of target recognition, using the ROI local point cloud method reduces the iteration times of scene point cloud and template point cloud registration, and improve the efficiency of industrial robot’s destacking, sorting and palletizing.