Monte Carlo localization: efficient position estimation for mobile robots
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Multi-View Stereo Reconstruction and Scene Flow Estimation with a Global Image-Based Matching Score
International Journal of Computer Vision
Fuzzy logic-based real-time robot navigation in unknown environment with dead ends
Robotics and Autonomous Systems
A simple modelling of complex environments for mobile robots
International Journal of Intelligent Systems Technologies and Applications
A curvilinear collision avoidance scheme for interactive 3D gaming environments
ACE '08 Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology
Behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigation
Robotics and Autonomous Systems
Laser and vision sensing for obstacle avoidance and target seeking for a simple mobile robot
International Journal of Intelligent Systems Technologies and Applications
Map-based navigation in mobile robots
Cognitive Systems Research
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Active map learning for autonomous robots exploring in unknown environments is an essential and vital issue for robot navigation. However, many factors limit a robot's ability to learn accurate map models in practice. To enhance its learning capability, this paper proposed a binocular vision system, developed using IP web cameras and MATLAB image processing toolbox. It integrates knowledge from computer vision and robot navigation. Through the binocular vision system, binocular images are captured and analysed so that feature objects or shapes are identified. Together with sonar readings, this information is used in collision avoidance and map building. It is shown that the capability of active map learning is effectively realised by adopting this binocular vision system.