Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Estimating the absolute position of a mobile robot using position probability grids
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
RoboCup 2004
Fuzzy uncertainty modeling for grid based localization of mobile robots
International Journal of Approximate Reasoning
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In this work, a novel method called Fuzzy Reverse Monte Carlo Localization (Fuzzy R-MCL) for global localization of autonomous mobile agents in the robotic soccer domain is proposed to overcome the uncertainty in the sensors, environment and the motion model. R-MCL is a hybrid method based on both Markov Localization(ML) and Monte Carlo Localization(MCL) where the ML module finds the region where the robot should be and MCL predicts the geometrical location with high precision by selecting samples in this region. In this work, a fuzzy approach is embedded in this method, to improve flexibility, accuracy and robustness. In addition to using Fuzzy membership functions in modeling the uncertainty of the grid cells and samples, different heuristics are used to enable the adaptation of the method to different levels of noise and sparsity. The method is very robust and fast and requires less computational power and memory compared to similar approaches and is accurate enough for high level decision making which is vital for robot soccer.