Spoofing-Resilient LiDAR-GPS Factor Graph Localization with Chimera Authentication

TL;DR

This paper presents a factor graph-based framework that fuses LiDAR and GPS data to detect and mitigate GPS spoofing attacks, ensuring robust vehicle localization even under malicious signal interference.

Contribution

It introduces a novel integration of LiDAR odometry with GPS pseudorange measurements and a chi-squared detector for spoofing detection within a factor graph framework.

Findings

Achieves position errors under 5 meters in nominal conditions.

Successfully bounds errors within odometry drift during spoofing.

Demonstrates robustness across multiple trajectories and Monte Carlo simulations.

Abstract

Many vehicle platforms typically use sensors such as LiDAR or camera for locally-referenced navigation with GPS for globally-referenced navigation. However, due to the unencrypted nature of GPS signals, all civilian users are vulner-able to spoofing attacks, where a malicious spoofer broadcasts fabricated signals and causes the user to track a false position fix. To protect against such GPS spoofing attacks, Chips-Message Robust Authentication (Chimera) has been developed and will be tested on the Navigation Technology Satellite 3 (NTS-3) satellite being launched later this year. However, Chimera authentication is not continuously available and may not provide sufficient protection for vehicles which rely on more frequent GPS measurements. In this paper, we propose a factor graph-based state estimation framework which integrates LiDAR and GPS while simultaneously detecting and mitigating spoofing attacks experienced between consecutive Chimera authentications. Our proposed framework combines GPS pseudorange measurements with LiDAR odometry to provide a robust navigation solution. A chi-squared detector, based on pseudorange residuals, is used to detect and mitigate any potential GPS spoofing attacks. We evaluate our method using real-world LiDAR data from the KITTI dataset and simulated GPS measurements, both nominal and with spoofing. Across multiple trajectories and Monte Carlo runs, our method consistently achieves position errors under 5 m during nominal conditions, and successfully bounds positioning error to within odometry drift levels during spoofed conditions.