@article{ref1,
title="Real-time vehicle positioning and mapping using graph optimization",
journal="Sensors (Basel)",
year="2021",
author="Das, Anweshan and Elfring, Jos and Dubbelman, Gijs",
volume="21",
number="8",
pages="e2815-e2815",
abstract="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.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s21082815",
url="http://dx.doi.org/10.3390/s21082815"
}