研究了复杂环境下不同形状物体的快速识别、定位以及表面检测,旨在满足智能机器在线作业时对复杂环境中的目标进行同步性抓取以及表面检测等需求,讨论了异形物体的多目标快速识别、定位、立体匹配及点云后处理算法。首先,基于稳健主成分分析识别出场景中的新增目标,再运用改进k均值聚类对各目标进行图像定位。然后,通过支持向量机筛选出感兴趣区域,并借助外极线约束进行一维搜索获取双目图像中的待匹配区域,快速获得局部三维点云。最后,进行特定的点云去噪处理以减小误差。实验结果表明,相比于传统方法,本文算法有效缩短了程序运行时间,并有效抑制了由复杂背景引起的各种噪声,提高了在复杂环境下获取点云的精度和自适应性,是一种稳健、有效、快速的三维点云获取算法。

Fast recognizing, positioning and surface detection of multi-shaped objects in complex environment are studied to satisfy the requirement of smart machines, which is expected to grab the objects or inspect surface defection in complex environment in real time. Fast recognition, positioning, stereo matching and post-processing algorithm of point clouds are discussed. At first, new targets in the scene are recognized by robust principal component analysis, and the image location of the targets is accurately acquired by improved k-means clustering algorithm. Then, the region of interest is screened out by support vector machine, and one-dimensional searching is carried out by epipolar restriction to obtain the regions to be matched in binocular images, and local three-dimensional point clouds are quickly obtained. Finally, special denoising operation of point clouds is conducted to reduce the error. The experiment results indicate that the proposed algorithm effectively reduces the running time of the process and effectively reduces all the noises caused by complex backgrounds, and improves the accuracy and adaptability of point clouds acquisition in complex environment, and it is a robust, effective and fast algorithm for three-dimensional point clouds acquisition.