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Vision perception plays a key role in the research on humanoid robot. A new version of particle filters called coevolution based adaptive particle filters (CEAPF) is proposed for robot localization. Using vision and odometer, a robust perception model extracting from environmental features, which are unscented by Kalman filter, is established by effective fixed scale feature-transformation method. Dimensional feature points matching algorithms are used to match the feature points based on KD-Tree to merge a species of cooperative coevolution competition mechanisms into particle filters. Coevolution adaptive particle filters are proposed to track the different assumptions and the samples in inter-species evolution can be moved towards the larger desired regions by using the crossover and mutation operators in evolutionary computation. Finally, results of success and precision localization are shown by experiment.

Research of Global Localization for Humanoid Robot Based on Vision