A Fast Ground Segmentation Method of LiDAR Point Cloud From Coarse-to-Fine

Guo, Dongbing; Yang, Guohui; Qi, Baoling; Wang, Chunhui

doi:10.1109/jsen.2022.3225293

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…Bogoslavskyi et al [23,34] transformed point clouds generated by mechanically rotating multi-line LiDAR into range images and subsequently utilized the feature relationships between adjacent pixels in the image for point cloud segmentation. Guo et al [35] combined the concentric zone model with the range images method and proposed a coarse-to-fine ground point segmentation method. On the other hand, region-growing-based point cloud segmentation algorithms were proposed earlier.…”

Section: Adjacent Points and Local Featuresmentioning

confidence: 99%

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

Deng,

Chen,

Jiang

2024

Electronics

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Ground segmentation is a crucial task in the field of 3D LiDAR perception for autonomous driving. It is commonly used as a preprocessing step for tasks such as object detection and road extraction. However, the existing ground segmentation algorithms often struggle to meet the requirements of robustness and real-time performance due to significant variations in ground slopes and flatness across different scenes, as well as the influence of objects such as grass, flowerbeds, and trees in the environment. To address these challenges, this paper proposes a staged real-time ground segmentation algorithm. The proposed algorithm not only achieves high real-time performance but also exhibits improved robustness. Based on a concentric zone model, the algorithm filters out reflected noise points and vertical non-ground points in the first stage, improving the validity of the fitted ground plane. In the second stage, the algorithm effectively addresses the issue of undersegmentation of ground points through three steps: ground plane fitting, ground plane validity judgment, and ground plane repair. The experimental results on the SemanticKITTI dataset demonstrate that the proposed algorithm outperforms the existing methods in terms of segmentation results.

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Section: Adjacent Points and Local Featuresmentioning

confidence: 99%

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

Deng,

Chen,

Jiang

2024

Electronics

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“…The LWR method can adapt to the distribution and density of different data points, as well as retain the local characteristics and nonlinear trends of the data. It is widely used in various fields such as automatic driving [32], software engineering quantity estimation [33], and computer tomography [34].…”

Section: Introductionmentioning

confidence: 99%

Velocity Estimation for Space Infrared Dim Targets Based on Multi-Satellite Observation and Robust Locally Weighted Regression

Zhang,

Rao,

Zhang

et al. 2023

Remote Sensing

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Velocity estimation of space moving targets is a key part of space situational awareness. However, most of the existing methods do not consider the satellite observation process, and the performance mainly depends on the preset target motion state, which has great limitations. To accurately obtain the motion characteristics of space infrared dim targets in space-based infrared detection, a velocity estimation method based on multi-satellite observation and robust locally weighted regression is proposed. Firstly, according to parameters such as satellite position, satellite attitude angle, and sensor line of sight, the overall target observation model from the sensor coordinate frame to the Earth-centered inertial coordinate frame is established, and the pixel coordinates of the target imaging point are extracted using the gray-weighted centroid method. Then, combined with the least squares criterion, the position sequence of the space target is obtained. Finally, a robust locally weighted regression operation is performed on the target position sequence to estimate the velocity. This study verified the feasibility of the proposed method through simulation examples, with the results showing that the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of the method were only 0.0733 m/s and 1.6640 m/s without measurement error. Moreover, the velocity estimation accuracy was better than that of other methods in most scenarios. In addition, the estimation accuracy under the impact of various measurement errors was analyzed, and it was found that the pixel coordinate extraction error had the greatest impact on velocity estimation accuracy. The proposed method provides a technical basis for the recognition of space infrared dim moving targets.

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