A Localization Evaluation System for Autonomous Vehicle

被引：0

作者：

Yin, Yuan ^{[1
]}

Chen, Wanmi ^{[1
]}

Wang, Yang ^{[1
]}

Jin, Hongzhou ^{[1
]}

机构：

[1] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200072, Peoples R China

来源：

INTELLIGENT COMPUTING AND INTERNET OF THINGS, PT II | 2018年 / 924卷

关键词：

Autonomous vehicle; Localization evaluation; Grid hypothesis model; Kullback-Leibler divergence; Fourier transform;

D O I：

10.1007/978-981-13-2384-3_22

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：236 / 245

页数：10

共 11 条

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