Satellite Image Based Cross-view Localization for Autonomous Vehicle

TL;DR

This paper presents a novel satellite image-based cross-view localization method for autonomous vehicles that achieves high accuracy without relying on expensive 3D maps, using geometric feature extraction and pose refinement techniques.

Contribution

It introduces a new approach with geometric-align features, pose-aware learning, and iterative pose refinement, surpassing traditional image retrieval methods in accuracy.

Findings

Median spatial error within 1 meter

Median angular error within 1 degree

Validated on KITTI and Ford datasets

Abstract

Existing spatial localization techniques for autonomous vehicles mostly use a pre-built 3D-HD map, often constructed using a survey-grade 3D mapping vehicle, which is not only expensive but also laborious. This paper shows that by using an off-the-shelf high-definition satellite image as a ready-to-use map, we are able to achieve cross-view vehicle localization up to a satisfactory accuracy, providing a cheaper and more practical way for localization. While the utilization of satellite imagery for cross-view localization is an established concept, the conventional methodology focuses primarily on image retrieval. This paper introduces a novel approach to cross-view localization that departs from the conventional image retrieval method. Specifically, our method develops (1) a Geometric-align Feature Extractor (GaFE) that leverages measured 3D points to bridge the geometric gap between ground and overhead views, (2) a Pose Aware Branch (PAB) adopting a triplet loss to encourage pose-aware feature extraction, and (3) a Recursive Pose Refine Branch (RPRB) using the Levenberg-Marquardt (LM) algorithm to align the initial pose towards the true vehicle pose iteratively. Our method is validated on KITTI and Ford Multi-AV Seasonal datasets as ground view and Google Maps as the satellite view. The results demonstrate the superiority of our method in cross-view localization with median spatial and angular errors within $1$ meter and $1^\circ$, respectively.