Ground Feature Point Matching Based Global Localization for Driverless Vehicles
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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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