Research on real-time positioning and map construction technology of intelligent car based on ROS

R. Liu, Zhiwei Guan, Bin Li, Guoqiang Wen, B. Liu
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引用次数: 0

Abstract

Real-time localization and map construction (SLAM) is a key technology to realize autonomous navigation of smart cars, which mainly solves the problems of mobile robots in mapping, positioning and path planning. This paper introduces and analyzes SLAM. By comparing three different mapping algorithms, gmapping, hector, and cartographer, and through analysis and comparison experiments, the gmapping-based algorithm is finally used for mapping. On the basis of AMCL positioning, global and local path planning is carried out through A* algorithm and DWA algorithm to realize autonomous navigation and obstacle avoidance functions. And the experimental verification was carried out under the autolabor smart car. The experiment proved that using this algorithm, autolabor can perform accurate pose estimation, map construction and autonomous navigation in an unfamiliar environment.
基于ROS的智能汽车实时定位与地图构建技术研究
实时定位与地图构建(SLAM)是实现智能汽车自主导航的关键技术,主要解决移动机器人在地图绘制、定位和路径规划等方面的问题。本文对SLAM进行了介绍和分析。通过比较gmpapping、hector和cartographer三种不同的制图算法,并通过分析和对比实验,最终采用基于gmpapping的算法进行制图。在AMCL定位的基础上,通过A*算法和DWA算法进行全局和局部路径规划,实现自主导航和避障功能。并在自动驾驶智能车上进行了实验验证。实验证明,该算法可以在陌生环境下进行准确的姿态估计、地图构建和自主导航。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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