Point-based object recognition in RGB-D images

Abstract

To operate autonomously a robot system needs among others to perceive the environment and to recognize the scene objects. In particular, nowadays an RGB-D sensor can be applied for vision-based perception. In this paper, two data-driven RGB-D image analysis steps, required for a reliable 3D object recognition process, are studied and appropriate algorithmic solutions are proposed. Clusters of 3D point features are detected in order to represent 3D object hypotheses. Particular clusters act as initial rough object hypotheses, allowing to constrain the subsequent model-based search for more distinctive object features in the image, like surface patches, textures and edges. In parallel, a 3D surface-based occupancy map is created, that delivers surface segments for the object recognition process. Test results are reported on various approaches to point feature detection and description, and point cloud processing.