基于矢量地图的自主代客泊车定位算法研究  

Research on AVP Localization Algorithm Based on Vector Map

作　　者：张天奇 曹容川 Zhang Tianqi;Cao Rongchuan(General R&D Center,China FAW Corporation Limited,Changchun 130013)

机构地区：[1]中国第一汽车股份有限公司研发总院,长春130013

出　　处：《汽车工程师》2023年第1期7-13,共7页Automotive Engineer

摘　　要：为实现自主代客泊车系统高精度定位功能,提出一种基于矢量地图的多传感器融合定位算法。采用视觉语义特征对自动驾驶车辆周围的环境进行表达,使定位效果能够实现长期的一致性,并将其与矢量化地图数据进行匹配,实现基于视觉输入的绝对位姿的解算。为实现上述功能,提出一种新的匹配策略以解决观测数据和矢量化地图数据表达不一致问题。同时,通过设计合理的误差函数将姿态估计问题建模为非线性优化问题,以获得高精度的解算结果。此外,为提升定位结果的鲁棒性,使用基于误差状态的卡尔曼滤波器将视觉匹配定位的结果与惯性测量单元(IMU)、轮速计的测量值进行融合,得到一种紧耦合的模块化定位方法。基于真实数据的验证结果表明,提出的算法可行,与主流方法相比可以获得更高的性能。In order to realize high-precision localization of Autonomous Valet Parking(AVP)system,this paper proposed a multi-sensor fusion localization algorithm based on vector map.In order to achieve long-term consistency of the localization effect,this paper employed visual semantic features to express the environment around the autonomous vehicle,and matched it with the vectorized map items to solve the absolute pose based on visual input.To achieve the above functions,this paper proposed a novel matching strategy to address the inconsistency of expression between observational data and vectorized map data.Meanwhile,by designing a reasonable error function and modelling the pose estimation problem as a nonlinear optimization problem,the method can achieve high-precision solution results.In addition,in order to improve the robustness of the localization results,this paper employed the Kalman filter based on the error state to fuse the results of the visual matching localization with the measured values of the IMU and wheel speedometer,and realized a tightly coupled modular localization method.The verification results based on real data show that the algorithm proposed in this paper is feasible in both theory and practice,and it can be seen from the comparison test with the mainstream methods that this method can achieve higher performance.

关 键 词：矢量地图 匹配定位 自主代客泊车 卡尔曼滤波 

分 类 号：TP319[自动化与计算机技术—计算机软件与理论]