This work presents a technique for localization of a smart infrastructure node, consisting of a fisheye camera, in a prior map. These cameras can detect objects that are outside the line of sight of the autonomous vehicles (AV) and send that information to AVs using V2X technology. However, in order for this information to be of any use to the AV, the detected objects should be provided in the reference frame of the prior map that the AV uses for its own navigation. Therefore, it is important to know the accurate pose of the infrastructure camera with respect to the prior map. Here we propose to solve this localization problem in two steps, \textit{(i)} we perform feature matching between perspective projection of fisheye image and bird's eye view (BEV) satellite imagery from the prior map to estimate an initial camera pose, \textit{(ii)} we refine the initialization by maximizing the Mutual Information (MI) between intensity of pixel values of fisheye image and reflectivity of 3D LiDAR points in the map data. We validate our method on simulated data and also present results with real world data.
翻译:这项工作在以前的地图中提出了智能基础设施节点的定位技术,由鱼眼照相机组成。这些照相机可以探测自主飞行器(AV)视线外的物体,并利用V2X技术将信息传送给AV。然而,为了使这些信息对AV有任何用处,应在AV用于自身导航的前地图参考框架中提供所探测到的物体。因此,必须了解基础设施相机相对于上一张地图的准确位置。我们在这里提议分两个步骤解决这一本地化问题,即\textit{(i)}我们执行对鱼眼图像前景投影和鸟眼视卫星图像的特征匹配,从以前的地图中估计初始摄影姿势,\textit{(ii)}我们通过尽可能扩大对鱼眼图像像像像像像等离子值强度与地图数据3DLDAR点反射度之间的相互信息(MI),改进初始化。我们验证了模拟数据的方法,并以真实世界数据展示结果。