Semantic mapping using object-class segmentation of RGB-D images

For task planning and execution in unstructured environments, a robot needs the ability to recognize and localize relevant objects. When this information is made persistent in a semantic map, it can be used, e. g., to communicate with humans. In this paper, we propose a novel approach to learning such maps. Our approach registers measurements of RGB-D cameras by means of simultaneous localization and mapping. We employ random decision forests to segment object classes in images and exploit dense depth measurements to obtain scaleinvariance. Our object recognition method integrates shape and texture seamlessly. The probabilistic segmentation from multiple views is filtered in a voxel-based 3D map using a Bayesian framework. We report on the quality of our objectclass segmentation method and demonstrate the benefits in accuracy when fusing multiple views in a semantic map.