A Staged Real-Time Ground Segmentation Algorithm of 3D LiDAR Point Cloud

Deng, Weiye; Chen, Xiaoping; Jiang, Jingwei

doi:10.3390/electronics13050841

Cited by 4 publications

(2 citation statements)

References 41 publications

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“…Three-dimensional scanning, which encompasses technologies such as LiDAR, allows for detailed measurements and high-fidelity models, particularly with terrestrial setups where the equipment is stationary. For instance, a higher-resolution sensor influences the detection capabilities, distinguishing between different types of surfaces markings, obstacles, and traffic [95][96][97]. Conversely, the spatial resolution in Remote Sensing, which involves a broader range of technologies including aerial imagery and satellite photos, can vary significantly.…”

Section: Application Of Advanced Surveying Methods (Advanced Surveying)mentioning

confidence: 99%

Civil Integrated Management (CIM) for Advanced Level Applications to Transportation Infrastructure: A State-of-the-Art Review

Taheri,

Sobanjo

2024

Infrastructures

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The recent rise in the applications of advanced technologies in the sustainable design and construction of transportation infrastructure demands an appropriate medium for their integration and utilization. The relatively new concept of Civil Integrated Management (CIM) is such a medium; it enhances the development of digital twins for infrastructure and also embodies various practices and tools, including the collection, organization, and data-management techniques of digital data for transportation infrastructure projects. This paper presents a comprehensive analysis of advanced CIM tools and technologies and categorizes its findings into the following research topics: application of advanced surveying methods (Advanced Surveying); geospatial analysis tools for project planning (Geospatial Analysis); multidimensional virtual design models (nD Modeling); Integrated Geospatial and Building Information Modeling (GeoBIM); and transportation infrastructure maintenance and rehabilitation planning (Asset Management). Despite challenges such as modeling complexity, technology investment, and data security, the integration of GIS, BIM, and artificial intelligence within asset-management systems hold the potential to improve infrastructure’s structural integrity and long-term performance through automated monitoring, analysis, and predictive maintenance during its lifetime.

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Section: Application Of Advanced Surveying Methods (Advanced Surveying)mentioning

confidence: 99%

Civil Integrated Management (CIM) for Advanced Level Applications to Transportation Infrastructure: A State-of-the-Art Review

Taheri,

Sobanjo

2024

Infrastructures

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“…This is important for applications that require fast decision making, such as collision avoidance systems in self-driving cars; moreover, these are significant in LiDAR point cloud data for object detection and tracking. LiDAR ground thresholding [17,18] is an important step in processing LiDAR point cloud data, especially for applications like navigation and mapping. The goal is to separate ground points from non-ground points.…”

Section: Introductionmentioning

confidence: 99%

An Advanced Approach to Object Detection and Tracking in Robotics and Autonomous Vehicles Using YOLOv8 and LiDAR Data Fusion

Dai,

Kim,

Lee

2024

Electronics

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Accurately and reliably perceiving the environment is a major challenge in autonomous driving and robotics research. Traditional vision-based methods often suffer from varying lighting conditions, occlusions, and complex environments. This paper addresses these challenges by combining a deep learning-based object detection algorithm, YOLOv8, with LiDAR data fusion technology. The principle of this combination is to merge the advantages of these technologies: YOLOv8 excels in real-time object detection and classification through RGB images, while LiDAR provides accurate distance measurement and 3D spatial information, regardless of lighting conditions. The integration aims to apply the high accuracy and robustness of YOLOv8 in identifying and classifying objects, as well as the depth data provided by LiDAR. This combination enhances the overall environmental perception, which is critical for the reliability and safety of autonomous systems. However, this fusion brings some research challenges, including data calibration between different sensors, filtering ground points from LiDAR point clouds, and managing the computational complexity of processing large datasets. This paper presents a comprehensive approach to address these challenges. Firstly, a simple algorithm is introduced to filter out ground points from LiDAR point clouds, which are essential for accurate object detection, by setting different threshold heights based on the terrain. Secondly, YOLOv8, trained on a customized dataset, is utilized for object detection in images, generating 2D bounding boxes around detected objects. Thirdly, a calibration algorithm is developed to transform 3D LiDAR coordinates to image pixel coordinates, which are vital for correlating LiDAR data with image-based object detection results. Fourthly, a method for clustering different objects based on the fused data is proposed, followed by an object tracking algorithm to compute the 3D poses of objects and their relative distances from a robot. The Agilex Scout Mini robot, equipped with Velodyne 16-channel LiDAR and an Intel D435 camera, is employed for data collection and experimentation. Finally, the experimental results validate the effectiveness of the proposed algorithms and methods.

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Ground-LIO: enhanced LiDAR-inertial odometry for ground robots based on ground optimization

Zhu,

Zou,

Yun

et al. 2024

Meas. Sci. Technol.

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In the field of LiDAR-based Simultaneous Localization and Mapping (SLAM), the potential of ground point clouds to enhance pose estimation in mobile robots has yet to be fully realized. This paper focuses on leveraging ground point clouds to improve the performance of LiDAR-Inertial Odometry (LIO) systems for ground-based mobile robots. We begin by analyzing the characteristics of ground point clouds and the typical types of noise that affect their extraction and utilization. Ground point clouds are then extracted from denoised data. Given the generally local planar nature of ground point clouds, we propose a segmentation-and-refitting approach to process them. This method reduces the computational burden of residual calculation in pose estimation by avoiding redundant plane fitting. Additionally, we introduce a data structure designed for the efficient management and utilization of ground point clouds obtained through segmentation and refitting. This structure is particularly suited to the ground point cloud data produced by our method, enabling efficient access and registration through the continuous maintenance and consolidation of local plane parameters. Our method has been integrated into advanced LIO systems\cite{faster-lio}, and experimental results on challenging datasets demonstrate its promising performance.

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A Staged Real-Time Ground Segmentation Algorithm of 3D LiDAR Point Cloud

Cited by 4 publications

References 41 publications

Civil Integrated Management (CIM) for Advanced Level Applications to Transportation Infrastructure: A State-of-the-Art Review

Civil Integrated Management (CIM) for Advanced Level Applications to Transportation Infrastructure: A State-of-the-Art Review

An Advanced Approach to Object Detection and Tracking in Robotics and Autonomous Vehicles Using YOLOv8 and LiDAR Data Fusion

Ground-LIO: enhanced LiDAR-inertial odometry for ground robots based on ground optimization

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