Increasingly, autonomous vehicles (AVs) are becoming a reality, such as the Advanced Driver Assistance Systems (ADAS) in vehicles that assist drivers in driving and parking functions with vehicles today. The localization problem for AVs relies primarily on multiple sensors, including cameras, LiDARs, and radars. Manufacturing, installing, calibrating, and maintaining these sensors can be very expensive, thereby increasing the overall cost of AVs. This research explores the means to improve localization on vehicles belonging to the ADAS category in a platooning context, where an ADAS vehicle follows a lead "Smart" AV equipped with a highly accurate sensor suite. We propose and produce results by using a filtering framework to combine pose information derived from vision and odometry to improve the localization of the ADAS vehicle that follows the smart vehicle.

翻译：随着高级驾驶辅助系统（ADAS）等技术的应用，自动驾驶汽车正逐步成为现实，目前车辆中的ADAS系统可辅助驾驶员完成驾驶和泊车功能。自动驾驶汽车的定位问题主要依赖于多传感器系统，包括摄像头、激光雷达和毫米波雷达。这些传感器的制造、安装、标定与维护成本高昂，从而增加了自动驾驶汽车的整体成本。本研究探索了在队列行驶场景中提升ADAS类别车辆定位精度的方法——在该场景中，一辆ADAS车辆跟随搭载高精度传感器套件的“智能”领航车。我们提出了一种过滤框架，通过将视觉与里程计信息融合来优化跟随智能车辆的ADAS车辆定位，并给出了相关实验结果。