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

IEEE Transactions on Robotics, vol. 40, pp. 4003–4023

Abstract

Accurate and robust vehicle localization in highly urbanized areas is challenging. Sensors are often corrupted in those complicated and large-scale environments. This article introducesgnssFGO, a global and online trajectory estimator that fuses global navigation satellite systems (GNSS) observations alongside multiple sensor measurements for robust vehicle localization. IngnssFGO, we fuse asynchronous sensor measurements into the graph with a continuous-time trajectory representation using Gaussian process (GP) regression. This enables querying states at arbitrary timestamps without strict state and measurement synchronization. Thus, the proposed method presents a generalized factor graph for multisensor fusion. To evaluate and study different GNSS fusion strategies, we fuse GNSS measurements in loose and tight coupling with a speed sensor, inertial measurement unit, and LiDAR-odometry. We employed datasets from measurement campaigns in Aachen, Düsseldorf, and Cologne 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 a classic multisensor fusion method fails due to sensor degradation. In a test sequence containing a 17-km route through Aachen, the proposed method results in a mean 2-D positioning error 0.48 m while fusing raw GNSS observations with LiDAR odometry in a tight coupling

Authors 4

1. Haoming Zhang Aachen Institute of Automatic Control (IRT) Faculty of Mechanical Engineering with the Institute of Automatic Control

   RWTH Aachen University · Erasmus MC · Delft University of Technology

   Affiliation as printed

   Institute of Automatic Control, Faculty of Mechanical Engineering, RWTH Aachen University, Aachen, Germany

   Department of BioMechanical Engineering, Delft University of Technology, and with the Department for Rehabilitation Medicine, Erasmus MC, Rotterdam, The Netherlands

   with the Institute of Automatic Control, Faculty of Mechanical Engineering, RWTH Aachen University, Aachen, Germany

2. Chih-Chun Chen Aachen Institute of Automatic Control (IRT) Faculty of Mechanical Engineering with the Institute of Automatic Control

   RWTH Aachen University · Erasmus MC · Delft University of Technology

   Affiliation as printed

   Institute of Automatic Control, Faculty of Mechanical Engineering, RWTH Aachen University, Aachen, Germany

   Department of BioMechanical Engineering, Delft University of Technology, and with the Department for Rehabilitation Medicine, Erasmus MC, Rotterdam, The Netherlands

   with the Institute of Automatic Control, Faculty of Mechanical Engineering, RWTH Aachen University, Aachen, Germany

3. Heike Vallery Aachen Institute of Automatic Control (IRT) Faculty of Mechanical Engineering with the Institute of Automatic Control

   RWTH Aachen University · Erasmus MC · Delft University of Technology

   Affiliation as printed

   Institute of Automatic Control, Faculty of Mechanical Engineering, RWTH Aachen University, Aachen, Germany

   Department of BioMechanical Engineering, Delft University of Technology, and with the Department for Rehabilitation Medicine, Erasmus MC, Rotterdam, The Netherlands

   with the Institute of Automatic Control, Faculty of Mechanical Engineering, RWTH Aachen University, Aachen, Germany

4. Timothy D. Barfoot

   University of Toronto

   Affiliation as printed

   University of Toronto Robotics Institute, Toronto, ON, Canada

   T. D. Barfoot is with the University of Toronto Robotics Institute, Toronto, Canada