方辉, 杨明, 杨汝清. 基于地面特征点匹配的无人驾驶车全局定位[J]. 机器人, 2010, 32(1): 55-60.
引用本文: 方辉, 杨明, 杨汝清. 基于地面特征点匹配的无人驾驶车全局定位[J]. 机器人, 2010, 32(1): 55-60.
FANG Hui, YANG Ming, YANG Ruqing. Ground Feature Point Matching Based Global Localization for Driverless Vehicles[J]. ROBOT, 2010, 32(1): 55-60.
Citation: FANG Hui, YANG Ming, YANG Ruqing. Ground Feature Point Matching Based Global Localization for Driverless Vehicles[J]. ROBOT, 2010, 32(1): 55-60.

基于地面特征点匹配的无人驾驶车全局定位

Ground Feature Point Matching Based Global Localization for Driverless Vehicles

  • 摘要: 针对室外环境特点,设计将摄像机安装在车辆底部,提出一种基于地面特征点的地图匹配法以获取车辆定位信息.定位方法分为两步:(1)手动控制车辆在环境中运行,保存RTK(real-time kinematic)-GPS、里程计和摄像机等传感器数据,离线自动创建地面特征点地图,并利用一种特殊的地图组织方式来提高地图搜索和匹配效率;(2)利用地图匹配对车辆进行定位,其中采用一种基于M估计加权ICP(iterative closest point)算法的特征点对应和匹配参数求解方法,并进一步采用UKF(unscented Kalman filter)算法融合地图匹配和航位推算的结果以提高定位鲁棒性.实验结果表明了该方法的有效性.

     

    Abstract: Vehicle localization is achieved by a ground feature points based map matching approach,in which a camera is fixed downward on the bottom of the vehicle according to the outdoor environmental conditions.The proposed approach includes two steps:(1) a vehicle is manually controlled to move in an environment,recording sensor data from RTK(real-time kinematic)-GPS,odometry and camera to produce a ground feature point map automatically in an off-line manner.A special map organization is used to increase the efficiency of map search and matching.(2) vehicle localization is realized by map matching method,in which a M-estimator weighted ICP(iterative closest point) algorithm is utilized to match feature points and compute matching parameters.Furthermore,map matching result is fused with dead-reckoning by UKF(unscented Kalman filter) to achieve higher robustness.Experimental results demonstrate the effectiveness of the proposed approach.

     

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