基于双目视觉的纸箱尺寸测量立体匹配方法研究

针对智能仓库物资自动化出入库流程，对于储存于纸箱中物资的出入库，需进行纸箱尺寸测量、纸箱拆解、物资倾倒、物资拣选等任务，因此首先应对纸箱进行无人化尺寸测量，提出自适应GrabCut的SGBM改进算法，实现纸箱尺寸自动测量。该方法首先完成双目相机的标定，利用NLM算法对采集的图片进行去噪处理，完成纸箱图片的立体校正，采用NCC、SGBM、AD-Census三种立体匹配算法得到纸箱的视差图，在分析其效果的基础上，提出基于模板匹配自适应GrabCut的SGBM改进算法用于纸箱立体匹配并得到其尺寸信息。实验结果表明，改进算法可以实现纸箱尺寸信息的精确测量，尺寸误差小于10 mm,满足实际的生产要求。

Research on three-dimensional matching method of carton size recognition based on binocular vision

For the automated access process of intelligent warehouse materials, for the access of materials stored in cartons, it is necessary to carry out tasks such as carton size measurement, carton disassembly, material dumping, material picking, etc. Therefore, the carton should firstly be unmanned size measurement, and the SGBM improvement algorithm of Adaptive GrabCut is proposed to realize the automatic measurement of carton size. The method firstly completes the calibration of binocular camera, uses NLM algorithm to denoise the collected pictures, completes the stereo correction of carton pictures, uses NCC, SGBM, AD-Census three stereo matching algorithms to get the parallax map of carton, on the basis of analyzing the effect, proposes the SGBM improvement algorithm based on the template matching Adaptive GrabCut to be used for the stereo matching of carton and get its size information. The experimental results show that the improved algorithm can realize the accurate measurement of carton size information, and the size error is less than 10 mm, which meets the actual production requirements.