机器人2012，Vol.34Issue(4)：440-448,9.DOI:10.3724/SP.J.1218.2012.00440
基于曲率特征的自主车辆地图匹配定位方法
A Novel Localization Approach for Autonomous Vehicles Based on Map Matching with Curvature Features
摘要
Abstract
Using the curvature features, a novel map-matching based localization approach for autonomous vehicles is proposed. By computing the scale-invariant curvature integral and its correlation of autonomous vehicle's historical and reference trajectories for matching, the proposed approach can effectively eliminate the mismatch problem caused by odometer calibration parameters bias and azimuth estimation errors in dead-reckoning (DR). Firstly, we integrate the inertial measurement unit output, steering angles, and wheel speed measurements from four ABS (anti-lock braking system) sensors by using the extended Kalman filter in order to estimate the autonomous vehicle's position and orientation, which are then used to select the candidate matching segments from digital maps. Then, a map matching algorithm based on spatial curvature features is proposed to accomplish segment matching, and matching points are determined according to the changes in curvature and yaw. Finally, these matching points are further utilized as the measurements of the unscented Kalman filter to update the filter and achieve high-precision estimation of pose. The experimental results in the real road condition show that the proposed approach is able to realize map matching effectively, reduce the accumulative error of autonomous vehicles in DR, and estimate the pose of autonomous vehicles accurately for long-range navigation even if the GPS (global positioning system) signal occasionally fails.关键词
自主车辆/地图匹配/尺度不变特征/无迹卡尔曼滤波Key words
autonomous vehicle/ map matching/ scale-invariant feature/ unscented Kalman filter分类
信息技术与安全科学引用本文复制引用
苏奎峰,邓志东,黄振..基于曲率特征的自主车辆地图匹配定位方法[J].机器人,2012,34(4):440-448,9.基金项目
国家自然科学基金资助项目(90820305,60775040) （90820305,60775040）
国家863计划资助项目(2011AA041001). （2011AA041001）
