In this work, we propose and evaluate a pose-graph optimization-based real-time multi-sensor fusion framework for vehicle positioning using low-cost automotive-grade sensors. Pose-graphs can model multiple absolute and relative vehicle positioning sensor measurements and can be optimized using nonlinear techniques. We model pose-graphs using measurements from a precise stereo camera-based visual odometry system, a robust odometry system using the in-vehicle velocity and yaw-rate sensor, and an automotive-grade GNSS receiver. Our evaluation is based on a dataset with 180 km of vehicle trajectories recorded in highway, urban, and rural areas, accompanied by postprocessed Real-Time Kinematic GNSS as ground truth. We compare the architecture’s performance with (i) vehicle odometry and GNSS fusion and (ii) stereo visual odometry, vehicle odometry, and GNSS fusion; for offline and real-time optimization strategies. The results exhibit a 20.86% reduction in the localization error’s standard deviation and a significant reduction in outliers when compared with automotive-grade GNSS receivers.
Real-Time Vehicle Positioning and Mapping Using Graph Optimization
2021-04-16
Das , A , Elfring , J & Dubbelman , G 2021 , ' Real-Time Vehicle Positioning and Mapping Using Graph Optimization ' , Sensors , vol. 21 , no. 8 , 2815 . https://doi.org/10.3390/s21082815
Article (Journal)
Electronic Resource
English
DDC: | 629 |
Robust Vehicle Positioning in Multipath Environments Based on Graph Optimization
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