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 scale-invariance. 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 object-class segmentation method and demonstrate the benefits in accuracy when fusing multiple views in a semantic map.