Conceptual spatial representations for indoor mobile robots

We present an approach for creating conceptual representations of human-made indoor environments using mobile robots. The concepts refer to spatial and functional properties of typical indoor environments. Following findings in cognitive psychology, our model is composed of layers representing maps at different levels of abstraction. The complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition. The system also incorporates a linguistic framework that actively supports the map acquisition process, and which is used for situated dialogue. Finally, we discuss the capabilities of the integrated system

SIMULTANEOUS LOCALIZATION AND MAPPING IN UNMODIFIED ENVIRONMENTS USING STEREO VISION

Abstract: In this paper we describe an approach that builds three dimensional maps using visual landmarks extracted from images of an unmodified environment. We propose a solution to the Simultaneous Localization and Mapping (SLAM) problem for autonomous mobile robots using visual landmarks. Our map is represented by a set of three dimensional landmarks referred to a global reference frame, each landmark contains a visual descriptor that partially differentiates it from others. Significant points extracted from stereo images are used as natural landmarks, in particular we employ SIFT features found in the environment. We estimate both the map and the path of the robot using a Rao-Blackwellized particle filter, thus the problem is decomposed into two parts: one estimation over robot paths using a particle filter, and N independent estimations over landmark positions, each one conditioned on the path estimate. We actively track visual landmarks at a local neighbourhood and select only those that are more stable. When a visual feature has been observed from a significant number of frames it is then integrated in the filter. By this procedure, the total number of landmarks in the map is reduced, compared to prior approaches. Due to the tracking of each landmark, we obtain different examples that represent the same natural landmark. We use this fact to improve data association. Finally, efficient resampling techniques have been applied, which reduces the number of particles needed and avoids the particle depletion problem.