Global localization is a very fundamental and challenging problem in Robotic Soccer. Here, the main aim is to find the best method which is very robust and fast and requires less computational resources and memory compared to similar approaches and is precise enough for robot soccer games and technical challenges. In this work, the Reverse Monte Carlo localization (R-MCL) method is introduced. The algorithm is designed for fast, precise and robust global localization of autonomous robots in the robotic soccer domain, to overcome the uncertainties in the sensors, environment and the motion model. R-MCL is a hybrid method based on Markov localization (ML) and Monte Carlo localization (MCL), where the ML based module finds the region where the robot should be and the MCL based part predicts the geometrical location with high precision by selecting samples in this region. It is called Reverse since the MCL routine is applied in a reverse manner in this algorithm. In this work, this method is tested on a challenging data set that is used by many other researchers and compared in terms of error rate against different levels of noise, and sparsity. Additionally, the time required to recover from kidnapping and the processing time of the methods are tested and compared. According to the test results R-MCL is a considerable method against high sparsity and noise. It is preferable when its recovery from kidnapping and processing times are considered. It gives robust and fast but relatively coarse position estimations against imprecise and inadequate perceptions, and coarse action data, including regular misplacements, and false perceptions. (c) 2007 Elsevier B.V. All rights reserved.