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.
@article{arxiv.1510.09171,
title = {Accurate Vision-based Vehicle Localization using Satellite Imagery},
author = {Hang Chu and Hongyuan Mei and Mohit Bansal and Matthew R. Walter},
journal= {arXiv preprint arXiv:1510.09171},
year = {2015}
}
Comments
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