2015/10/30 by Chu, Hang, Mei, Hongyuan, Bansal, Mohit +1
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Robotics (cs.RO)
paper · doi:10.48550/arxiv.1510.09171
We propose a method for accurately localizing ground vehicles with the aid of satellite imagery. Our approach takes a ground image as input, and outputs the location from which it was taken on a georeferenced satellite image. We perform visual localization by estimating the co-occurrence probabilities between the ground and satellite images based on a ground-satellite feature dictionary. The method is able to estimate likelihoods over arbitrary locations without the need for a dense ground image database. We present a ranking-loss based algorithm that learns location-discriminative feature projection matrices that result in further improvements in accuracy. We evaluate our method on the Malaga and KITTI public datasets and demonstrate significant improvements over a baseline that performs exhaustive search.