High-accurate localization is crucial for the safety and reliability of autonomous driving, especially for the information fusion of collective perception that aims to further improve road safety by sharing information in a communication network of ConnectedAutonomous Vehicles (CAV). In this scenario, small localization errors can impose additional difficulty on fusing the information from different CAVs. In this paper, we propose a RANSAC-based (RANdom SAmple Consensus) method to correct the relative localization errors between two CAVs in order to ease the information fusion among the CAVs. Different from previous LiDAR-based localization algorithms that only take the static environmental information into consideration, this method also leverages the dynamic objects for localization thanks to the real-time data sharing between CAVs. Specifically, in addition to the static objects like poles, fences, and facades, the object centers of the detected dynamic vehicles are also used as keypoints for the matching of two point sets. The experiments on the synthetic dataset COMAP show that the proposed method can greatly decrease the relative localization error between two CAVs to less than 20cmas far as there are enough vehicles and poles are correctly detected by bothCAVs. Besides, our proposed method is also highly efficient in runtime and can be used in real-time scenarios of autonomous driving.
翻译:高度精确的本地化对于自主驾驶的安全性和可靠性至关重要,特别是对于集聚集体认识,目的是通过在连接自动车辆通信网络(CAV)中共享信息来进一步改善道路安全。在这种情况下,小本地化错误会给从不同的CAV中提取信息带来更多困难。在本文中,我们提议使用一个基于RANSAC(RANDY Sample Consulation)的方法来纠正两个CAV之间的相对本地化错误,以方便CAV之间的信息融合。与以前基于LIDAR(LIDAR)的本地化算法不同,后者仅考虑静态环境信息,这种方法还利用动态物体进行本地化,因为CAVS之间的实时数据共享。具体地说,除了电线杆、栅栏和法眼外,被检测到的动态飞行器的物体中心也被用作匹配两套点的关键点。合成数据集的实验表明,拟议的方法可以大大减少两个CAVAVA到不那么高效的飞行器之间的相对本地化误差。此外,在实时的飞行器中也能够正确地探测到,在高时段。