LIDAR-Based High Reflective Landmarks (HRL)s For Vehicle Localization in an HD Map

Ghallabi, Farouk; Mittet, Marie-Anne; El-Haj-Shhade, Ghayath; Nashashibi, Fawzi

doi:10.1109/itsc.2019.8917057

Cited by 14 publications

(7 citation statements)

References 18 publications

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“…By combining GPS, IMU sensors, and leveraging particle filtering and Kalman filtering, they introduced a high-precision localization algorithm. Ghallabi et al [11] matched features like lane markings, road signs, and guardrail reflectors with HD maps, resulting in a fusion localization system based on mapmatching using particle filtering. Beyond feature-based matching localization, Javanmardi et al [12] introduced a method that aligns LiDAR observations with two distinct formats of abstract maps to achieve precise vehicle positioning.…”

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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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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“…Using markers is attractive because it allows reliable, accurate localization and can easily be implemented even on computationally-constrained embedded devices. For this reason, they are still used for vehicle localization with markers on guardrails [2], for automated robots with many tube-shaped reflectors placed in the environment [3], or for localization in rough terrain [4]. However, setting up the reflector markers can be tedious.…”

Section: Related Workmentioning

confidence: 99%

When Geometry is not Enough: Using Reflector Markers in Lidar SLAM

Kurz¹,

Scherer²,

Biber³

et al. 2022

Preprint

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Lidar-based SLAM systems perform well in a wide range of circumstances by relying on the geometry of the environment. However, even mature and reliable approaches struggle when the environment contains structureless areas such as long hallways. To allow the use of lidar-based SLAM in such environments, we propose to add reflector markers in specific locations that would otherwise be difficult. We present an algorithm to reliably detect these markers and two approaches to fuse the detected markers with geometry-based scan matching. The performance of the proposed methods is demonstrated on real-world datasets from several industrial environments.

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“…A longer buffer should therefore result in fewer ambiguous matchings. The buffer should, however, be small enough as the smoother state estimates are transformed in a rigid manner by the optimization, see equation (10). Figure 7 shows the localization error of the vehicle for different buffer lengths.…”

Section: Buffer Size Influencementioning

confidence: 99%

“…As lidars measure distances, they are especially interesting for localization when combined with maps. Lidars have not only been used with dense maps [7], [8], [9] but also with sparse feature maps [10], [11]. From lidars, particular landmarks can be detected not only using the Cartesian information, but also thanks to the intensity data returned by most lidars.…”

Section: Introductionmentioning

confidence: 99%

Improved Data Association Using Buffered Pose Adjustment for Map-Aided Localization

Welte

Zinoune

2020

IEEE Robot. Autom. Lett.

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Maps provide an important source of information for autonomous vehicles. They can be used along with cameras and lidars to localize the vehicle. This requires the ability to correctly associate observations to features referenced in the map. The problem is all the more difficult than all observations are not necessarily referenced, and all map features might not be detectable with the embedded sensors. This paper presents an adjustment technique that enables to increase the number of associations that can be made while limiting the chance of obtaining wrong associations. This is achieved by matching observations in batches in a buffer and matching them regularly. Periodically, the observation buffer is used to adjust the trajectory used to match observations. This is done without making any assumption on the association between observations and map features through a likelihood maximization process. The adjusted trajectory then provides the best associations that are used for real-time localization. The method was tested with data recorded on public roads using an experimental vehicle. The results show that thanks to the trajectory adjustment step and the use of an observation buffer, the number of associations that can be made is increased. This also results in greater localization accuracy and consistency with an average error of 0.7 meters at 50 Hz using road markings and traffic signs. This work was carried out within the shared laboratory SIVALab between Renault and Heudiasyc (UTC/CNRS) and co-financed by the Hauts-de-France region (ERDF grant). The equipment is from the Equipex ROBOTEX

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LIDAR-Based High Reflective Landmarks (HRL)s For Vehicle Localization in an HD Map

Cited by 14 publications

References 18 publications

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

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

When Geometry is not Enough: Using Reflector Markers in Lidar SLAM

Improved Data Association Using Buffered Pose Adjustment for Map-Aided Localization

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