  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.
