Using Real-Time Stereo Vision for Mobile Robot Navigation
Autonomous Robots
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Appearance-Based SLAM for Mobile Robots
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
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Expert Systems with Applications: An International Journal
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This paper addresses the problem of exploring and mapping an unknown environment using a robot equipped with a stereo vision sensor. The main contribution of our work is a fully automatic mapping system that operates without the use of active range sensors (such as laser or sonic transducers), can operate on-line and can consistently produce accurate maps of large-scale environments. Our approach implements a Rao-Blackwellised particle filter (RBPF) to solve the simultaneous localization and mapping problem and uses efficient data structures for real-time data association, mapping, and spatial reasoning. We employ a hybrid map representation that infers 3D point landmarks from image features to achieve precise localization, coupled with occupancy grids for safe navigation. We demonstrate two exploration approaches, one based on a greedy strategy and one based on an iteratively deepening strategy. This paper describes our framework and implementation, and presents our exploration method, and experimental results illustrating the functionality of the system.