Localization of a Smart Infrastructure Fisheye Camera in a Prior Map for Autonomous Vehicles
This addresses the need for precise camera localization to enable autonomous vehicles to use infrastructure-detected objects for navigation, though it is incremental as it builds on existing localization techniques.
This work tackles the problem of localizing a smart infrastructure fisheye camera in a prior map for autonomous vehicles, achieving accurate pose estimation through a two-step method involving feature matching and mutual information refinement, validated on simulated and real-world data.
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.