A supervised learning approach for fast object recognition from RGB-D data

Object recognition serves obvious purposes in assisted living environments, where robotic devices can be used as companions to assist humans in need. The recent introduction of vision based sensors, which are able to extract depth sensing information about the environment, in addition to the traditional RGB video, presents new opportunities and challenges for more accurate object recognition. The current work, presents an object recognition approach that uses RGB-D point cloud data and a novel feature extraction methodology, in combination with well-known supervised learning algorithms, to achieve accurate, real-time recognition of a large number of objects. In our experiments, we use a dataset of household objects organized into 51 categories, and evaluate the recognition accuracy and time efficiency of a set of different supervised learning methods.