基于多传感信息融合的智能车辆定位方法

针对高精度定位系统中地图的重要性问题,将定位问题分为无地图定位与基于地图定位,分别对智能车辆的定位问题进行探索.对研究的智能车辆、传感器及其定位问题进行建模分析,再对该平台实施传感器校准以减小系统误差.对于无地图定位问题,利用扩展卡尔曼滤波算法将里程计与惯性测量单元(IMU)数据相融合,通过试验证明航迹推测法存在累计误...

A Localization Method Based on Multi-Sensor Information Fusion for Intelligent Vehicles

Due to the importance of the map in the high-precision positioning system, localizationin this paper was divided into mapless localization and map-based localization,and the intelligent vehicle positioning was explored.Initially,the intelligent vehicle platform,sensors and their positioning were modeled and analyzed and then the sensor calibration was performed on the platform to reduce the system error. Then for the mapless positioning, the extended kalman filter algorithm was used to fuse data of the odometer and the inertial measurement unit(IMU).The accumulated error was found in the experiment which indicates that the track prediction method was not applicable to the estimation of long distance pose. Finally, for map-based positioning,the laser sensor was employed to construct the indoor environment map. According to the particle filter algorithm based on Monte Carlo method,the odometer,IMU and laser data information were fused to conduct experiments of indoor positioning. The results show that the map-based localization method can correct the accumulated errors. In this case, the localization success rate can reach more than 70%, and the success rate of angle estimation can reach 90% for linear trajectory, which proves the importance of the map in the positioning system.