Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
Experiences with an interactive museum tour-guide robot
Artificial Intelligence - Special issue on applications of artificial intelligence
Mobile Robot Relocation from Echolocation Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
A fast algorithm for the maximum clique problem
Discrete Applied Mathematics - Sixth Twente Workshop on Graphs and Combinatorial Optimization
Fastslam: a factored solution to the simultaneous localization and mapping problem with unknown data association
The Reverse Monte Carlo localization algorithm
Robotics and Autonomous Systems
SLAM in O(logn) with the Combined Kalman-Information Filter
Robotics and Autonomous Systems
Divide and Conquer: EKF SLAM in
IEEE Transactions on Robotics
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A feature-based method for global localization of mobile robot using a concept of matching signatures is presented. A group of geometric features, their geometric constraints invariant to frame transform, and location dependent constraints, together are utilized in defining signature of a feature. Plausible global poses are found out by matching signatures of observed features with signatures of global map features. The concept of matching signatures is so developed that the proposed method provides a very efficient solution for global localization. Worst-case complexity of the method for estimating and verifying global poses is linear with the size of global reference map. It will also be shown that with the approach of random sampling the proposed algorithm becomes linear with both the size of global map and number of observed features. In order to avoid pose ambiguity, simultaneous tracking of multiple pose hypotheses staying within the same framework of the proposed method is also addressed. Results obtained from simulation as well as from real world experiment demonstrate the performance and effectiveness of the method.