Tian Jican, Zhao Liming, Zhou Wenlong, Chen Zailu, Gao Xing
Appl Opt. 2025 Feb 10;64(5):A31-A41. doi: 10.1364/AO.534520.
This paper takes industrial robot disordered object grasping as its research background and proposes an object recognition method based on binocular laser three-dimensional (3D) scanning imaging for the characteristics of grasping objects with complex structures, single surface colors, and scattered stacking. First, to address the issue of incomplete data in the 3D reconstruction of stacked scenes caused by self-obscuration and mutual occlusion of objects, this paper proposes a binocular laser scanning 3D imaging method that utilizes a rapid fusion of multi-source point cloud data. Secondly, this paper reports improved point cloud matching of a pose estimation algorithm based on the point pair feature (PPF) to achieve accurate pose estimation of scattered stacked objects. In the offline training stage, the hidden point removal (HPR) method is introduced to impose visibility constraints, eliminating interference from occluded points and achieving a more accurate global description of the model. In the online matching stage, farthest point sampling (FPS) is applied to the scene point cloud to improve the uniformity of the sampling results. Through Hough voting, candidate poses are quickly obtained. The rough estimation of the object pose was completed by adjusting the grasping pose screening strategy to remove the mismatched poses. Finally, the iterative closest point (ICP) algorithm is used to optimize the poses and obtain more accurate pose results. The experimental results demonstrate that the object information reconstruction completeness of the proposed method is significantly improved compared with the original binocular vision and monocular laser vision system, and the recognition time and average error are reduced compared to the original PPF algorithm. Furthermore, the matching success rate is 100% in 20 cluttered scenes. In the actual grasping experiments, the success rate of grasping is 93.3%, which verifies the proposed method's effectiveness for cluttered occlusion.
本文以工业机器人无序物体抓取为研究背景，针对结构复杂、表面颜色单一且呈散乱堆叠的抓取物体特性，提出一种基于双目激光三维（3D）扫描成像的物体识别方法。首先，针对物体自遮挡和相互遮挡导致堆叠场景3D重建数据不完整的问题，本文提出一种利用多源点云数据快速融合的双目激光扫描3D成像方法。其次，本文报道了基于点对特征（PPF）的姿态估计算法的改进点云匹配，以实现对散乱堆叠物体的精确姿态估计。在离线训练阶段，引入隐藏点去除（HPR）方法施加可见性约束，消除遮挡点的干扰，实现对模型更精确的全局描述。在在线匹配阶段，对场景点云应用最远点采样（FPS），以提高采样结果的均匀性。通过霍夫投票，快速获得候选姿态。通过调整抓取姿态筛选策略去除不匹配姿态，完成物体姿态的粗估计。最后，使用迭代最近点（ICP）算法优化姿态，获得更精确的姿态结果。实验结果表明，与原始双目视觉和单目激光视觉系统相比，该方法的物体信息重建完整性显著提高，与原始PPF算法相比，识别时间和平均误差降低。此外，在20个杂乱场景中匹配成功率为100%。在实际抓取实验中，抓取成功率为93.3%，验证了该方法对杂乱遮挡情况的有效性。
