Point Cloud Segmentation Algorithm Based on Improved Euclidean Clustering

Abstract

Point cloud segmentation is a crucial technique for object recognition and localization, widely employed in various applications such as point cloud registration, 3D reconstruction, object recognition, and robotic grasping. However, in practical scenarios, challenges such as sparse object features, significant pose variations, or instances of object adhesion, stacking, and occlusion can lead to difficulties and poor stability in point cloud segmentation. In response to these challenges, this paper proposes a novel point cloud segmentation algorithm based on enhanced Euclidean clustering. To address the issue of over-segmentation and under-segmentation in cases of object adhesion and stacking in unordered scenes, a method is introduced for discriminating collision points based on normal angle constraints. Subsequently, an adaptive search radius is employed to determine the clustering distance threshold, enhancing the algorithm’s ability to handle different object arrangements. Finally, the collision points are reintegrated into the clustering results to ensure the completeness of the segmented target point cloud. Experimental results using the ROBI public dataset and real-world point cloud segmentation scenarios demonstrate that the proposed algorithm effectively resolves challenges associated with the segmentation of adhering and stacking objects. The improved algorithm exhibits higher accuracy and robustness in multi-object segmentation across diverse scenes.