Xiao Zhongyang, Yang Diange, Wen Tuopu, Jiang Kun, Yan Ruidong
State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China.
Sensors (Basel). 2020 Mar 27;20(7):1870. doi: 10.3390/s20071870.
Real-time vehicle localization (i.e., position and orientation estimation in the world coordinate system) with high accuracy is the fundamental function of an intelligent vehicle (IV) system. In the process of commercialization of IVs, many car manufacturers attempt to avoid high-cost sensor systems (e.g., RTK GNSS and LiDAR) in favor of low-cost optical sensors such as cameras. The same cost-saving strategy also gives rise to an increasing number of vehicles equipped with High Definition (HD) maps. Rooted upon these existing technologies, this article presents the concept of Monocular Localization with Vector HD Map (MLVHM), a novel camera-based map-matching method that efficiently aligns semantic-level geometric features in-camera acquired frames against the vector HD map in order to achieve high-precision vehicle absolute localization with minimal cost. The semantic features are delicately chosen for the ease of map vector alignment as well as for the resiliency against occlusion and fluctuation in illumination. The effective data association method in MLVHM serves as the basis for the camera position estimation by minimizing feature re-projection errors, and the frame-to-frame motion fusion is further introduced for reliable localization results. Experiments have shown that MLVHM can achieve high-precision vehicle localization with an RMSE of 24 cm with no cumulative error. In addition, we use low-cost on-board sensors and light-weight HD maps to achieve or even exceed the accuracy of existing map-matching algorithms.
高精度实时车辆定位（即在世界坐标系中估计位置和方向）是智能车辆（IV）系统的基本功能。在智能车辆商业化过程中，许多汽车制造商试图避免使用高成本的传感器系统（如实时动态全球导航卫星系统和激光雷达），而青睐低成本的光学传感器，如摄像头。同样的成本节约策略也导致越来越多的车辆配备了高清（HD）地图。基于这些现有技术，本文提出了基于矢量高清地图的单目定位（MLVHM）概念，这是一种新颖的基于摄像头的地图匹配方法，它能有效地将摄像头采集帧中的语义级几何特征与矢量高清地图对齐，从而以最小的成本实现高精度车辆绝对定位。为便于地图矢量对齐以及为了抵御遮挡和光照波动，精心选择了语义特征。MLVHM中有效的数据关联方法通过最小化特征重投影误差为摄像头位置估计奠定了基础，并且进一步引入帧间运动融合以获得可靠的定位结果。实验表明，MLVHM能够实现高精度车辆定位，均方根误差为24厘米，且无累积误差。此外，我们使用低成本车载传感器和轻量级高清地图来达到甚至超越现有地图匹配算法的精度。
