2015/10/30 by Hang Chu, Chu, Hang, Hongyuan Mei +5
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Robotics (cs.RO) #cs.CV #cs.RO
paper · pdf · doi:10.48550/arxiv.1510.09171
9 pages, 8 figures. Full version is submitted to ICRA 2016. Short version is to appear at NIPS 2015 Workshop on Transfer and Multi-Task Learning
arxiv created 2015/10/30 · arxiv updated 2015/11/02
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.