GNSS/Multi-Sensor Fusion Using Continuous-Time Factor Graph Optimization for Robust Localization

TL;DR

This paper presents GNSS-FGO, a continuous-time factor graph approach that fuses multiple sensors, including GNSS, to achieve robust vehicle localization in urban environments where sensor data can be unreliable.

Contribution

It introduces a novel continuous-time factor graph framework for multi-sensor fusion that handles asynchronous data and improves urban localization robustness.

Findings

Achieves a mean 2D positioning error of 0.48m in urban tests

Enables robust localization where traditional methods fail due to sensor degradation

Successfully fuses GNSS with lidar odometry in tight coupling

Abstract

Accurate and robust vehicle localization in highly urbanized areas is challenging. Sensors are often corrupted in those complicated and large-scale environments. This paper introduces GNSS-FGO, an online and global trajectory estimator that fuses GNSS observations alongside multiple sensor measurements for robust vehicle localization. In GNSS-FGO, we fuse asynchronous sensor measurements into the graph with a continuous-time trajectory representation using Gaussian process regression. This enables querying states at arbitrary timestamps so that sensor observations are fused without requiring strict state and measurement synchronization. Thus, the proposed method presents a generalized factor graph for multi-sensor fusion. To evaluate and study different GNSS fusion strategies, we fuse GNSS measurements in loose and tight coupling with a speed sensor, IMU, and lidar-odometry. We employed datasets from measurement campaigns in Aachen, Duesseldorf, and Cologne in experimental studies and presented comprehensive discussions on sensor observations, smoother types, and hyperparameter tuning. Our results show that the proposed approach enables robust trajectory estimation in dense urban areas, where the classic multi-sensor fusion method fails due to sensor degradation. In a test sequence containing a 17km route through Aachen, the proposed method results in a mean 2D positioning error of 0.48m while fusing raw GNSS observations with lidar odometry in a tight coupling.