Home Furniture Detection by Geometric Characterization by Autonomous Service Robots

International audienceService robots are nowadays more and more common on diverse environments. In order to provide useful services, robots must not only identify different objects but also understand their use and be able to extract characteristics that make useful an object. In this work, a framework is presented for recognize home furniture by analyzing geometrical features over point clouds. A fast and efficient method for horizontal and vertical planes detection is presented, based on the histograms of 3D points acquired from a Kinect like sensor onboard the robot. Horizontal planes are recovered according to height distribution on 2D histograms, while vertical planes with a similar approach over a projection on the floor (3D histograms). Characteristics of points belonging to a given plane are extracted in order to match with planes from furniture pieces in a database. Proposed approach has been proved and validated in home like environments with a mobile robotic platform