A Novel Detection Strategy for Kidnapped Robot Problem in Monte Carlo Localization Using Corridor-Type Map

Abstract

This paper proposed a novel method to detect the kidnapped robot problem event in Monte Carlo Localizationin corridor-like map. The proposed method is aimed towards overcoming two commmon drawbacks from existing methods of detection, namely the inability to stay accurate across wide array of particles’ convergence level, and a high dependency on the success of recovery process. This objective is achived by combining the difference in particle’s weight, maximum current weight, and difference in particles’ standard deviation. The addition of these two parameters makes the proposed method to be superior to pure maximum current weight parameter for kidnapping detection. A series of simulation tests using corridor-like map are executed to test the claim. These simulations show that the proposed method outperforms the maximum current weight parameter in terms of accuracy, ability to detect kidnapping during early stage of localization, and independency towards the success of the re-localization process.