Map-Based Localization Method for Autonomous Vehicles Using 3D-LIDAR

Wang, Liang; Zhang, Yihuan; Wang, Jun

doi:10.1016/j.ifacol.2017.08.046

Cited by 77 publications

(21 citation statements)

References 3 publications

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“…The use of classified point clouds, i.e., 3D data with a semantic meaning, has become popular in different fields of application, such as robotics [9,10], precision farming [11], autonomous driving [12,13], indoor navigation [14,15], urban planning [16,17], geospatial [18,19] and Cultural Heritage (CH) [5,20]. The heritage sector is slightly behind the other fields in term of automated and reliable procedures, due to the complexity and variability of the 3D data.…”

Section: Of 27mentioning

confidence: 99%

A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification

et al. 2020

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The recent years saw an extensive use of 3D point cloud data for heritage documentation, valorisation and visualisation. Although rich in metric quality, these 3D data lack structured information such as semantics and hierarchy between parts. In this context, the introduction of point cloud classification methods can play an essential role for better data usage, model definition, analysis and conservation. The paper aims to extend a machine learning (ML) classification method with a multi-level and multi-resolution (MLMR) approach. The proposed MLMR approach improves the learning process and optimises 3D classification results through a hierarchical concept. The MLMR procedure is tested and evaluated on two large-scale and complex datasets: the Pomposa Abbey (Italy) and the Milan Cathedral (Italy). Classification results show the reliability and replicability of the developed method, allowing the identification of the necessary architectural classes at each geometric resolution.

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Section: Of 27mentioning

confidence: 99%

A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification

et al. 2020

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“…Some algorithms [18] are extended variants of 2D SLAM methods that use point cloud segments and leveled range scans to achieve 3D perceptions. Wang et al [19] utilized 3D-LIDAR to precisely locate the autonomous vehicle, where the curbs information was detected to assist the pose estimation. Several experimental results were provided to demonstrate the accuracy and robustness of the method.…”

Section: Related Workmentioning

confidence: 99%

ClusterMap Building and Relocalization in Urban Environments for Unmanned Vehicles

Pan

Chen

et al. 2019

Sensors

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Map building and map-based relocalization techniques are important for unmanned vehicles operating in urban environments. The existing approaches require expensive high-density laser range finders and suffer from relocalization problems in long-term applications. This study proposes a novel map format called the ClusterMap, on the basis of which an approach to achieving relocalization is developed. The ClusterMap is generated by segmenting the perceived point clouds into different point clusters and filtering out clusters belonging to dynamic objects. A location descriptor associated with each cluster is designed for differentiation. The relocalization in the global map is achieved by matching cluster descriptors between local and global maps. The solution does not require high-density point clouds and high-precision segmentation algorithms. In addition, it prevents the effects of environmental changes on illumination intensity, object appearance, and observation direction. A consistent ClusterMap without any scale problem is built by utilizing a 3D visual–LIDAR simultaneous localization and mapping solution by fusing LIDAR and visual information. Experiments on the KITTI dataset and our mobile vehicle illustrates the effectiveness of the proposed approach.

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“…GNSS positioning errors can rise significantly in urban canyons due to multipath effects and non-line-of-sight (NLOS) [6,7]. To fill this gap, the LiDAR matching-based localization [3,4,8] with the prior map is a promising solution to provide the globally referenced and accurate positioning. Currently, the existing LiDAR map matching-based localization solution mainly relies on the 360 • rotating mechanical LiDAR, such as the 64 channels Velodyne HDL 64(about US$75,000) [9].…”

Section: Introductionmentioning

confidence: 99%

Low‐cost solid‐state LiDAR/inertial‐based localization with prior map for autonomous systems in urban scenarios

Zhong

Huang

Zhang

et al. 2022

IET Intelligent Trans Sys

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A low‐cost and accurate positioning solution is significant for the massive deployment of fully autonomous driving vehicles (ADV). Conventional mechanical LiDAR has proven its performance, but its high cost hinders the massive production of autonomous vehicles. This paper proposes a low‐cost LiDAR/inertial‐based localization solution for autonomous systems with prior maps in urban areas. Instead of relying on the costly mechanical LiDAR, this paper proposes to utilize the solid‐state LiDAR (SSL) with the prior map to estimate the position of the vehicle by matching the real‐time point clouds from the SSL and the prior map using the normal distribution transformation (NDT) algorithm. However, the field of view (FOV) of the SSL is signifcantly smaller than the conventional mechanical LiDAR, which can easily lead to failure during the NDT map matching. To fill this gap, this paper proposes to exploit the complementariness of the inertial measurement unit (IMU) and the SSL, where the IMU pre‐integration provides a coarse but high‐frequency initial guess to the map matching. To evaluate the effectiveness of the proposed method in this paper, the authors collect the dataset in two typical urban scenarios through a pedestrian hand‐hold and a vehicle driving condition. The results reveal that the SSL‐only‐based localization is significantly challenged in dynamic scenarios. With the help of the IMU, the robustness of the proposed method is significantly improved, achieving an accuracy of within 0.5 m. To show the sensitivity of the SSL‐based map matching against the initial guess of the state, this paper also presents the convergence results of the map matching under different levels of accuracy in terms of the initial guess.

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Map-Based Localization Method for Autonomous Vehicles Using 3D-LIDAR

Cited by 77 publications

References 3 publications

A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification

A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification

ClusterMap Building and Relocalization in Urban Environments for Unmanned Vehicles

Low‐cost solid‐state LiDAR/inertial‐based localization with prior map for autonomous systems in urban scenarios

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