TY - GEN
T1 - Sensor Fusion-Based Localization Framework for Autonomous Vehicles in Rural Forested Environments
AU - Matute, Jose
AU - Rodriguez-Arozamena, Mario
AU - Perez, Joshue
AU - Karimoddini, Ali
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - One major hurdle for the deployment of autonomous vehicles in rural environments is achieving accurate localization in areas with tree-canopied roads or outdated point cloud maps. The presence of limited visibility and high variability renders standalone sensor localization unreliable in such situations. To tackle these issues, this paper presents a sensor fusion-based localization framework that integrates data from GNSS, LiDAR, INS, and vehicle odometry. The proposed approach uses a loosely-coupled Extended Kalman Filter for sensor fusion and a weighted gate approach for accurate state estimations. Compared to a state-of-the-art technique, the proposed method achieves a reduction of around 71% in maximum lateral deviations. This method successfully enables a safe and reliable localization in challenging scenarios that are frequently found in the rural and inter-urban sectors.
AB - One major hurdle for the deployment of autonomous vehicles in rural environments is achieving accurate localization in areas with tree-canopied roads or outdated point cloud maps. The presence of limited visibility and high variability renders standalone sensor localization unreliable in such situations. To tackle these issues, this paper presents a sensor fusion-based localization framework that integrates data from GNSS, LiDAR, INS, and vehicle odometry. The proposed approach uses a loosely-coupled Extended Kalman Filter for sensor fusion and a weighted gate approach for accurate state estimations. Compared to a state-of-the-art technique, the proposed method achieves a reduction of around 71% in maximum lateral deviations. This method successfully enables a safe and reliable localization in challenging scenarios that are frequently found in the rural and inter-urban sectors.
UR - http://www.scopus.com/inward/record.url?scp=85186495213&partnerID=8YFLogxK
U2 - 10.1109/ITSC57777.2023.10422436
DO - 10.1109/ITSC57777.2023.10422436
M3 - Conference contribution
AN - SCOPUS:85186495213
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 1007
EP - 1013
BT - 2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023
Y2 - 24 September 2023 through 28 September 2023
ER -