onlineFGO: Online Continuous-Time Factor Graph Optimization with Time-Centric Multi-Sensor Fusion for Robust Localization in Large-Scale Environments

TL;DR

This paper introduces onlineFGO, a continuous-time graph optimization method that fuses multi-sensor data for robust vehicle localization in large-scale urban environments, achieving high accuracy and consistency.

Contribution

It presents a novel time-centric graph construction independent of spatial sensor measurements, enabling flexible multi-sensor fusion and improved localization in complex urban areas.

Findings

Achieved an average 2D localization error of 0.99m in urban scenarios.

Demonstrated robustness and consistency in large-scale urban environments.

Validated effectiveness through real-world experiments in Aachen city.

Abstract

Accurate and consistent vehicle localization in urban areas is challenging due to the large-scale and complicated environments. In this paper, we propose onlineFGO, a novel time-centric graph-optimization-based localization method that fuses multiple sensor measurements with the continuous-time trajectory representation for vehicle localization tasks. We generalize the graph construction independent of any spatial sensor measurements by creating the states deterministically on time. As the trajectory representation in continuous-time enables querying states at arbitrary times, incoming sensor measurements can be factorized on the graph without requiring state alignment. We integrate different GNSS observations: pseudorange, deltarange, and time-differenced carrier phase (TDCP) to ensure global reference and fuse the relative motion from a LiDAR-odometry to improve the localization consistency while GNSS observations are not available. Experiments on general performance, effects of different factors, and hyper-parameter settings are conducted in a real-world measurement campaign in Aachen city that contains different urban scenarios. Our results show an average 2D error of 0.99m and consistent state estimation in urban scenarios.