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.
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.
出处
《机器人》
EI
CSCD
北大核心
2010年第1期55-60,共6页
Robot
基金
教育部博士点基金资助项目(20070248097)
上海市科委科技创新行动计划资助项目(08510708300)
关键词
无人驾驶车辆
全局定位
地图匹配
ICP算法
UKF数据融合
driverless vehicle
global localization
map matching
ICP （iterative closest point） algorithm
UKF （unscented Kalman filter） data fusion
