Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Navigating Mobile Robots: Systems and Techniques
Navigating Mobile Robots: Systems and Techniques
CAD-Vision-Range-Based Self-Localization for Mobile Robot Using One Landmark
Journal of Intelligent and Robotic Systems
Mathematical Foundations of Navigation and Perception for an Autonomous Mobile Robot
RUR '95 Proceedings of the International Workshop on Reasoning with Uncertainty in Robotics
Landmark based global self-localization of mobile soccer robots
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Active single landmark based global localization of autonomous mobile robots
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
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This paper presents vision based self-localization of tiny autonomous mobile robots in a known but highly dynamic environment. The problem covers tracking the robot position with an initial estimate to global self-localization. The algorithm enables the robot to find its initial position and to verify its location during every movement. The global position of the robot is estimated using trilateration based techniques whenever distinct landmark features are extracted. Distance measurements are used as they require fewer landmarks compared to methods using angle measurements. However, the minimum required features for global position estimation are not available throughout the entire state space. Therefore, the robot position is tracked once a global position estimate is available. Extended Kalman filter is used to fuse information from multiple heterogeneous sensors. Simulation results show that the new method that combines the global position estimation with tracking results in significant performance gain.