基于分割树的移动机器人立体匹配研究

Research on stereo matching of mobile robot based on segment tree

针对移动机器人在作业时低纹理、重复纹理、反光的环境,提出了一种基于分割树的移动机器人立体匹配方法,提高深度恢复的计算速度和精度。首先针对移动机器人作业环境对图像进行预处理,减小因光线变化的影响;然后采用最小生成树对图像像素进行加权聚合,通过分割树进行优化同时分割图像;再对图像进行遮挡处理和视差精化,恢复遮挡区域和未提供视差的图像边缘区域的深度,提高深度恢复的精度。最后进行对比实验,在室内场景下新方法计算速度比ST-2方法提高了187.140%,能够为移动机器人提供语义信息;在Middlebury数据集上,新方法比ST-2方法计算速度提高了157.500%,匹配精度提高了66.547%。

This paper proposed a novel stereo matching method of the mobile robot based on segment tree to improve the speed and accuracy of depth recovery at the low texture,repeated texture and reflective environment.Firstly,it preprocessed the images for the mobile robot environment to reduce the influence of the light changes.In addition,this paper used the minimum spanning tree to weight the image pixels,and segmented the image by the segment tree.Then,it restored the depth of the occlusion area and the edge area that did not provide depth,and improved the accuracy of depth recovery.Finally,it carried out the comparison experiments.In indoor scenes,the calculation speed of this paper method was 187.140% faster than the ST-2 method,and semantic information could be provided for the mobile robot.On the Middlebury datasets,the calculation speed of this method was 157.500% faster than the ST-2 method,while the matching accuracy was improved 66.547%.