An Extended Monte Carlo Localization Approach Based on Collaborative Distributed Perception

摘要

In order to overcome the difficulty of a mobile robot to perform localization only with its onboard sensors,an extended Monte Carlo localization algorithm based on collaborative distributed perception is proposed.In the process of localization,the robot timely executes resampling from detection models of environmental sensors according to the changes of sampling distribution information entropy,effective sample size and sampling distribution uniformity before and after the robot's perceptive update,and thus the pose estimation uncertainty is reduced effectively.When the algorithm is implemented,color cameras are adopted as environmental sensors and their parameters are calibrated by the robot online.And then detection models of the cameras can be obtained based on the calibrated parameters.Experiment results illustrate the validity of the approach in solving problems of global localization and kidnapped robot.