Robots are rapidly evolving from factory workhorses to robot-companions. The future of robots, as our companions, is highly dependent on their abilities to understand, interpret and represent the environment in an efficient and consistent fashion, in a way that is comprehensible to humans. This paper is oriented in this direction. It suggests a hierarchical probabilistic representation of space that is based on objects. A global topological representation of places with object graphs serving as local maps is suggested. Experiments on place classification and place recognition are also reported in order to demonstrate the applicability of such a representation in the context of understanding space and thereby performing spatial cognition. Further, relevant results from user studies validating the proposed representation are also reported. Thus the theme of the work is-representation for spatial cognition.

This paper presents an experimental evaluation of different line extraction algorithms applied to 2D laser scans for indoor environments. Six popular algorithms in mobile robotics and computer vision are selected and tested. Real scan data collected from two office environments by using different platforms are used in the experiments in order to evaluate the algorithms. Several comparison criteria are proposed and discussed to highlight the advantages and drawbacks of each algorithm, including speed, complexity, correctness and precision. The results of the algorithms are compared with ground truth using standard statistical methods. An extended case study is performed to further evaluate the algorithms in a SLAM application.

Real-time 3D localization and mapping is eventually needed in many service robotic applications. Toward a light and practical SLAM algorithm, we focus on feature extraction via segmentation of range images. Using horizontal and vertical traces of the range matrix, 2D observed polygons are considered for calculation of a one-dimensional measure of direction, called Bearing Angle (BA). BA is the incident angle between the laser beam and edges of the observed polygon by the scanner in the selected direction. Based on this measure, two different approaches to range image segmentation, region-and edge-based, are proposed and evaluated through a set of standard analysis. It is experimentally shown that for navigation applications, edge based approaches are more efficient. Extensive tests on real robots prove BA-based segmentation is successful for SLAM.

This report presents an experimental evaluation of a plane extraction method using various line extraction algorithms. Four different algorithms are chosen, which are well known in mobile robotics and computer vision. Experiments are performed on two sets of 25 range images either obtained by simulation or acquired by a proprietary 3D laser scanner. The segmentation outcome of the simulated range images is measured in terms of an average segment classification ratio. Moreover, the speed of the method is measured to conclude on the suitability for service robot applications.

Abstract-This paper proposes an object classification framework based on a geometric grammar aimed for mobile robotic applications. The paper first discusses the geometric grammar as a compact representation form for object categories with primitive parts as its constituent elements. The paper then discusses the object classification implemented as parsing of primitive parts. In particular, two approaches are discussed that constrain the search space in order to render the parsing of the primitive parts practical. The two approaches are experimentally verified, first, for a generic object category of chair applied to real range images acquired with a range camera mounted on a mobile robot and, second, for multiple generic object categories applied to synthetic range images. The experimental results show the practicability of the framework.