Accurate Vision-based Vehicle Localization using Satellite Imagery
This addresses vehicle localization for autonomous navigation or mapping applications, but it is incremental as it builds on existing visual localization methods with specific enhancements.
The paper tackles the problem of localizing ground vehicles using satellite imagery by estimating co-occurrence probabilities between ground and satellite images, achieving significant accuracy improvements over an exhaustive search baseline on the Malaga and KITTI datasets.
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.