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

An efficient alternative approach for home furniture detection and localization by an autonomous mobile robot

International audienceIn order for service robots to help humans in daily home tasks, they need to have a better understanding of their environment. Therefore, detection and localization of home furniture becomes a very important capability for them. This paper presents an approach to detect typical home furniture by an RGB-D sensor mounted on a mobile robot. The approach is based on the analysis of discriminant features extracted from very easy to compute measures distributions. Over an offline learning phase, each piece of furniture is modeled according to their distributions of: height, color (H and S components) and normals. Then a sequence of distributions analysis are applied to the scene for selecting, according to learned models, the pieces of furniture with a high probability of being present. The point cloud is segmented according to model analysis and then segmented points are projected to the floor plane for clustering and noise removal. According to previous analysis and footprints of segmented points clouds, regions are then classified as a possible piece of furniture. Finally, the orientation and localization of the detected furnitures are obtained, using the footprint and their neighbor regions. The proposed approach has been proved to be very efficient in order to be incorporated on mobile robotic platforms

Use of Spherical and Cartesian Features for Learning and Recognition of the Static Mexican Sign Language Alphabet

The automatic recognition of sign language is very important to allow for communication by hearing impaired people. The purpose of this study is to develop a method of recognizing the static Mexican Sign Language (MSL) alphabet. In contrast to other MSL recognition methods, which require a controlled background and permit changes only in 2D space, our method only requires indoor conditions and allows for variations in the 3D pose. We present an innovative method that can learn the shape of each of the 21 letters from examples. Before learning, each example in the training set is normalized in the 3D pose using principal component analysis. The input data are created with a 3D sensor. Our method generates three types of features to represent each shape. When applied to a dataset acquired in our laboratory, an accuracy of 100% was obtained. The features used by our method have a clear, intuitive geometric interpretation

Use of Spherical and Cartesian Features for Learning and Recognition of the Static Mexican Sign Language Alphabet

The automatic recognition of sign language is very important to allow for communication by hearing impaired people. The purpose of this study is to develop a method of recognizing the static Mexican Sign Language (MSL) alphabet. In contrast to other MSL recognition methods, which require a controlled background and permit changes only in 2D space, our method only requires indoor conditions and allows for variations in the 3D pose. We present an innovative method that can learn the shape of each of the 21 letters from examples. Before learning, each example in the training set is normalized in the 3D pose using principal component analysis. The input data are created with a 3D sensor. Our method generates three types of features to represent each shape. When applied to a dataset acquired in our laboratory, an accuracy of 100% was obtained. The features used by our method have a clear, intuitive geometric interpretation