Beyond Line-of-Sight: Cooperative Localization Using Vision and V2X Communication

TL;DR

This paper introduces a decentralized vision and V2X communication-based localization method for connected vehicles, enabling accurate pose estimation in urban environments with occlusions, validated through real-world experiments and simulations.

Contribution

A novel decentralized observer algorithm that combines vision and V2X data for cooperative localization of connected vehicles in complex scenarios.

Findings

Proves local exponential stability of the proposed observer.

Demonstrates scalability through real vehicle experiments.

Validates effectiveness with large-scale simulations.

Abstract

Accurate and robust localization is critical for the safe operation of Connected and Automated Vehicles (CAVs), especially in complex urban environments where Global Navigation Satellite System (GNSS) signals are unreliable. This paper presents a novel vision-based cooperative localization algorithm that leverages onboard cameras and Vehicle-to-Everything (V2X) communication to enable CAVs to estimate their poses, even in occlusion-heavy scenarios such as busy intersections. In particular, we propose a novel decentralized observer for a group of connected agents that includes landmark agents (static or moving) in the environment with known positions and vehicle agents that need to estimate their poses (both positions and orientations). Assuming that (i) there are at least three landmark agents in the environment, (ii) each vehicle agent can measure its own angular and translational velocities as well as relative bearings to at least three neighboring landmarks or vehicles, and (iii) neighboring vehicles can communicate their pose estimates, each vehicle can estimate its own pose using the proposed decentralized observer. We prove that the origin of the estimation error is locally exponentially stable under the proposed observer, provided that the minimal observability conditions are satisfied. Moreover, we evaluate the proposed approach through experiments with real 1/10th-scale connected vehicles and large-scale simulations, demonstrating its scalability and validating the theoretical guarantees in practical scenarios.