Tunnel Facility Based Vehicle Localization in Highway Tunnel Using 3D LIDAR

Kim, Kyu‐Won; Im, Jun-Hyuck; Jee, Gyu-In

doi:10.1109/tits.2022.3160235

Cited by 19 publications

(5 citation statements)

References 30 publications

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“…The GNSS failure case is very commonly observed in a highway tunnel area. [42] used a 3D LiDAR to extract predefined tunnel facility points, and an EKF helps pose estimation by matching those features. [43] used a prior map for matching to help vehicle localization through visual odometry and road marking.…”

Section: A Map-based Approachmentioning

confidence: 99%

A Survey on Localization for Autonomous Vehicles

Kumar,

Muhammad

2023

IEEE Access

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Section: A Map-based Approachmentioning

confidence: 99%

A Survey on Localization for Autonomous Vehicles

Kumar,

Muhammad

2023

IEEE Access

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“…In dynamic environments, Zhou et al [23] proposed a feature detection and matching algorithm for dynamic environments based on RGB-D SLAM, achieving high-precision tracking and mapping in dynamic environments. In tunnel scenarios, Kim et al [24] presented a high-precision vehicle localization method based on tunnel facilities, employing point feature maps and probability distribution maps. Zhen et al [25] proposed a degradation feature model to estimate the feasibility of a given location in a prior map.…”

Section: Related Workmentioning

confidence: 99%

Degradation state detection and local map optimization for enhancing the SOTIF of map-matching-based fusion localization system

Cao,

Luo,

Wang

et al. 2023

Meas. Sci. Technol.

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In scenarios involving local map missing and local environmental changes, map-matching localization systems (MMLS) may experience degradation in localization due to the deficiency of texture and geometrical features. This consequently leads to a decrease in localization accuracy or divergence in a map-matching-based fusion localization system (MMBFLS). To address this issue and ensure the safety of the intended functionality of autonomous vehicles, this study proposes a degradation state detection and local map optimization-based fusion localization frame. To detect the degradation state of pose estimation in MMLS, the analysis of the mathematical mechanisms underlying the localization degradation in MMLS is conducted first. Subsequently, a degradation state detection method based on incremental matrix eigenvalue analysis is proposed, which leverages the magnitude of individual eigenvalues of the incremental matrix to determine the corresponding degradation state and isolates it accordingly. Furthermore, we employ sliding window-based factor graph optimization to maintain a local map that matches Light detection and ranging point clouds, compensating for the degraded states and thereby ensuring the localization performance of MMBFLS. Real-world sensor data is collected to validate the proposed method. The results indicate that the accuracy of the proposed degradation detection method reached as high as 94.67%. Moreover, the proposed localization framework can effectively ensure the localization accuracy of MMBFLS in typical highway scenarios.

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“…The 3D LiDAR is commonly used as the main sensor of conventional autonomous vehicles or AMRs. (13)(14)(15) However, with the development of hardware and deep learning, cameras for implementing pure vision have become a low-cost multifunction choice. For example, Tesla changed the vehicle autopilot assistance system into a pure vision version in 2022.…”

Section: Sensormentioning

confidence: 99%

Construction and Development of Visual Simultaneous Localization and Mapping System for Multi-person Dynamic Spaces

Pai,

Huang,

Chen

et al. 2023

Sensors and Materials

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In this study, we developed a visual simultaneous localization and mapping (SLAM) dynamic object filtering system applicable to multi-person dynamic spaces. When a visual mobile vehicle with an edge computing platform is used in the dynamic living space, the visual SLAM system will likely be disturbed by dynamic persons, leading to poor mapping and localization. We also implemented the dynamic object filtering function by preprocessing with a semantic segmentation neural network and designed a high-performance neural network architecture for the edge computing platform. The proposed design is capable of real-time operation to improve the effect of dynamic objects on the visual SLAM system.

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Tunnel Facility Based Vehicle Localization in Highway Tunnel Using 3D LIDAR

Cited by 19 publications

References 30 publications

A Survey on Localization for Autonomous Vehicles

A Survey on Localization for Autonomous Vehicles

Degradation state detection and local map optimization for enhancing the SOTIF of map-matching-based fusion localization system

Construction and Development of Visual Simultaneous Localization and Mapping System for Multi-person Dynamic Spaces

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