An Experimental Study on Relative and Absolute Pose Graph Fusion for Vehicle Localization

TL;DR

This study evaluates pose-graph fusion strategies combining GNSS and odometry for vehicle localization, demonstrating an 18% reduction in error variability but no change in bias across diverse driving environments.

Contribution

It provides an empirical analysis of relative and absolute pose graph fusion methods, highlighting the impact of error correlation in GNSS data on localization accuracy.

Findings

Error standard deviation reduced by 18% with fusion

Bias in error remains unchanged after fusion

Errors in GNSS readings are highly correlated in time

Abstract

In this work, we research and evaluate multiple pose-graph fusion strategies for vehicle localization. We focus on fusing a single absolute localization system, i.e. automotive-grade Global Navigation Satellite System (GNSS) at 1 Hertz, with a single relative localization system, i.e. vehicle odometry at 25 Hertz. Our evaluation is based on 180 Km long vehicle trajectories that are recorded in highway, urban and rural areas, and that are accompanied with post-processed Real Time Kinematic GNSS as ground truth. The results exhibit a significant reduction in the error's standard deviation by 18% but the bias in the error is unchanged, when compared to non-fused GNSS. We show that the underlying principle is the fact that errors in GNSS readings are highly correlated in time. This causes a bias that cannot be compensated for by using the relative localization information from the odometry, but it can reduce the standard deviation of the error.