Semantic Labelling of 3D Point Clouds using Spatial Object Constraints

The capability of dealing with knowledge from the real human environment is required for autonomous systems to perform complex tasks. The robot must be able to extract the objects from the sensors' data and give them a meaningful semantic description. In this paper a novel method for semantic labelling is presented. The method is based on the idea of connecting spatial information about the objects to their spatial relations to other entities. In this approach, probabilistic methods are used to deal with incomplete knowledge, caused by noisy sensors and occlusions. The process is divided into two stages. First, the spatial attributes of the objects are extracted and used for the object pre-classification. Second, the spatial constraints are taken into account for the semantic labelling process. Finally, we show that the use of spatial object constraints improves the recognition results.