Optimization of greenhouse tomato localization in overlapping areas

Abstract

Tomato localization is the main difficulty of tomato picking robots vision system. To provide robots vision system with the accurate position of tomatoes, this paper collects images with a binocular camera, provides the principle of binocular ranging, and improves the census stereo matching algorithm. The improved algorithm betters the area matching process: only the areas containing tomatoes are matched, more constraints are applied on the area matching, and the Localization of tomatoes in overlapping areas is optimized. Compared with stereo processing by semiglobal matching and mutual information (SGBM) algorithm and pyramid stereo matching network (PSMnet), the improved algorithm achieved an extremely small disparity error. The absolute error maximized at 4 pixels. The matching time for a single image was 10 ms at the most. In this way, the matching time is improved significantly. Experimental results show that the improved census matching algorithm provided tomato picking robots vision system with more accurate localization information, and greatly improved the picking efficiency.