A Localization Evaluation System for Autonomous Vehicle
The autonomous vehicle is a kind of intelligent robot. At present, there are many algorithms for autonomous vehicle localization, but few methods for localization evaluation. To solve this problem, a grid hypothesis model using the existing prior information of the surrounding environment and posterior information of current laser real-time collection of autonomous vehicle is proposed, Kullback–Leibler divergence and Fourier transform are methods to evaluate the current location results. The above two methods can give relatively accurate evaluation results and corresponding evaluation method can be selected according to the actual speed and accuracy requirements.
KeywordsAutonomous vehicle Localization evaluation Grid hypothesis model Kullback–Leibler divergence Fourier transform
This work is supported by Shanghai University, and we would like to appreciate the Prof. Wanmi Chen and M.E Yang Wang for the support of our paper.
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