The need for spatial representations and spatial reasoning is ubiquitous in AI – from robot planning and navigation, to interpreting visual inputs, to understanding natural language – in all these cases the need to represent and reason about spatial aspects of the world is of key importance. Related fields of research, such as geographic information science

The ability to represent knowledge about space and its position therein is crucial for a mobile robot. To this end, topological and semantic descriptions are gaining popularity for augmenting purely metric space representations. In this paper we present a multi-modal place classification system that allows a mobile robot to identify places and recognize semantic categories in an indoor environment. The system effectively utilizes information from different robotic sensors by fusing multiple visual cues and laser range data. This is achieved using a high-level cue integration scheme based on a Support Vector Machine (SVM) that learns how to optimally combine and weight each cue. Our multi-modal place classification approach can be used to obtain a real-time semantic space labeling system which integrates information over time and space. We perform an extensive experimental evaluation of the method for two different platforms and environments, on a realistic off-line database and in a live experiment on an autonomous robot. The results clearly demonstrate the effec-

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We present a robot localization system using biologically-inspired vision. Our system models two extensively studied human visual capabilities: (1) extracting the “gist” of a scene to produce a coarse localization hypothesis, and (2) refining it by locating salient landmark points in the scene. Gist is computed here as a holistic statistical signature of the image, yielding abstract scene classification and layout. Saliency is computed as a measure of interest at every image location, efficiently directing the time-consuming landmark identification process towards the most likely candidate locations in the image. The gist features and salient regions are then further processed using a Monte-Carlo localization algorithm to allow the robot to generate its position. We test the system in three different outdoor environments — building complex (38.4x54.86m area, 13966 testing images), vegetation-filled park (82.3x109.73m area, 26397 testing images), and open-field park (137.16x178.31m area, 34711 testing images) — each with its own challenges. The system is able to localize, on average, within 0.98, 2.63, and 3.46m, respectively, even with multiple kidnapped-robot instances.

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Recent developments in sensor technology have made it feasible to use mobile robots in several fields, but robots still lack the ability to accurately sense the environment. A major challenge to the widespread deployment of mobile robots is the ability to function autonomously, learning useful models of environmental features, recognizing environmental changes, and adapting the learned models in response to such changes. This article focuses on such learning and adaptation in the context of color segmentation on mobile robots in the presence of illumination changes. The main contribution of this article is a survey of vision algorithms that are potentially applicable to color-based mobile robot vision. We therefore look at algorithms for color segmentation, color learning and illumination invariance on mobile robot platforms, including approaches that tackle just the underlying vision problems. Furthermore, we investigate how the interdependencies between these modules and high-level action planning can be exploited to achieve autonomous learning and adaptation. The goal is to determine the suitability of the state-of-the-art vision algorithms for mobile robot domains, and to identify the challenges that still need to be addressed to enable mobile robots to learn and adapt models for color, so as to operate autonomously in natural conditions.

To my parents, Paul and B.J., for encouraging both wonder and accomplishment. To my wife, Sarah, for her unflagging love, support, and understanding. To all my friends, who have helped in innumerable ways.

What is a Map? • A map is a model of an environment that helps an agent plan and take action. • A topological map is useful for travel planning. • A metrical map is useful for inferring directions and distances. • Both must be learned from observations. 1 Scale of Space • Small-scale space is within the agent’s perceptual surround. – “visual space ” or “perceptual space” • Large-scale space has structure that must be integrated from the agent’s observations gathered over time and travel. – the “cognitive map” Local Metrical Mapping Works • In small-scale space, modern SLAM methods work extremely well with lasers. – Great progress with visual SLAM.

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