VVLoc: Prior-free 3-DoF Vehicle Visual Localization

TL;DR

VVLoc is a unified, neural network-based approach for vehicle localization that simultaneously performs topological and metric localization using multi-camera data, providing confidence measures and requiring minimal training data.

Contribution

It introduces VVLoc, a novel, efficient pipeline that combines topological and metric localization in a single network without relying on priors or complex data.

Findings

Achieves state-of-the-art accuracy on multiple datasets.

Operates efficiently with minimal training data.

Works effectively on challenging self-collected datasets.

Abstract

Localization is a critical technology in autonomous driving, encompassing both topological localization, which identifies the most similar map keyframe to the current observation, and metric localization, which provides precise spatial coordinates. Conventional methods typically address these tasks independently, rely on single-camera setups, and often require additional 3D semantic or pose priors, while lacking mechanisms to quantify the confidence of localization results, making them less feasible for real industrial applications. In this paper, we propose VVLoc, a unified pipeline that employs a single neural network to concurrently achieve topological and metric vehicle localization using multi-camera system. VVLoc first evaluates the geo-proximity between visual observations, then estimates their relative metric poses using a matching strategy, while also providing a confidence measure. Additionally, the training process for VVLoc is highly efficient, requiring only pairs of visual data and corresponding ground-truth poses, eliminating the need for complex supplementary data. We evaluate VVLoc not only on the publicly available datasets, but also on a more challenging self-collected dataset, demonstrating its ability to deliver state-of-the-art localization accuracy across a wide range of localization tasks.