Localization of a Smart Infrastructure Fisheye Camera in a Prior Map for Autonomous Vehicles

TL;DR

This paper introduces a two-step method to accurately localize a fisheye camera on infrastructure within a prior map, enabling better object detection and communication for autonomous vehicles.

Contribution

It presents a novel approach combining feature matching and mutual information maximization for precise infrastructure camera localization.

Findings

Effective in simulated environments

Validated with real-world data

Improves object localization accuracy for AVs

Abstract

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.