Global Navigation Satellite System/Inertial Measurement Unit/Camera/HD Map Integrated Localization for Autonomous Vehicles in Challenging Urban Tunnel Scenarios

Lane-level localization is critical for autonomous vehicles (AVs). However, complex urban scenarios, particularly tunnels, pose significant challenges to AVs’ localization systems. In this paper, we propose a fusion localization method that integrates multiple mass-production sensors, including Glob...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2024-06, Vol.16 (12), p.2230
Main Authors: Tao, Lu, Zhang, Pan, Gao, Kefu, Liu, Jingnan
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Autonomous vehicles
camera
Cameras
Deep learning
Digital maps
Errors
Extended Kalman filter
Global navigation satellite system
GNSS
HD map
High definition
IMU
Inertial navigation
Inertial platforms
Localization
Localization method
OEM
Redundancy
Roads & highways
Semantics
Sensors
Tunnels
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Summary:Lane-level localization is critical for autonomous vehicles (AVs). However, complex urban scenarios, particularly tunnels, pose significant challenges to AVs’ localization systems. In this paper, we propose a fusion localization method that integrates multiple mass-production sensors, including Global Navigation Satellite Systems (GNSSs), Inertial Measurement Units (IMUs), cameras, and high-definition (HD) maps. Firstly, we use a novel electronic horizon module to assess GNSS integrity and concurrently load the HD map data surrounding the AVs. This map data are then transformed into a visual space to match the corresponding lane lines captured by the on-board camera using an improved BiSeNet. Consequently, the matched HD map data are used to correct our localization algorithm, which is driven by an extended Kalman filter that integrates multiple sources of information, encompassing a GNSS, IMU, speedometer, camera, and HD maps. Our system is designed with redundancy to handle challenging city tunnel scenarios. To evaluate the proposed system, real-world experiments were conducted on a 36-kilometer city route that includes nine consecutive tunnels, totaling near 13 km and accounting for 35% of the entire route. The experimental results reveal that 99% of lateral localization errors are less than 0.29 m, and 90% of longitudinal localization errors are less than 3.25 m, ensuring reliable lane-level localization for AVs in challenging urban tunnel scenarios.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs16122230