Grid and homogeneity-based ground segmentation using light detection and ranging three-dimensional point cloud

Lin, Ciyun; Yang, Jie; Gong, Bowen; Liu, Hongchao; Sun, Ganghao

doi:10.1117/1.jrs.17.038506

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…This technique significantly enhances the accuracy of individual tree segmentation, achieving a notably higher average intersection over union than traditional methods. Lin et al 19 developed a method based on grid and uniformity for ground plane point identification and segmentation to improve the accuracy and computational efficiency of LiDAR environmental perception. They reduced the volume of point cloud data by combining conditional filtering with voxel filtering, used concentric circular grids to simplify data processing, and introduced a dynamic threshold model and a point cloud uniformity model to enhance the precision of ground point identification on uneven, fragmented, and sloped surfaces, as well as optimize recognition results in vegetated areas.…”

Section: Related Workmentioning

confidence: 99%

Multiscale region fusion algorithm for 3D plane segmentation

Yang,

Yao,

Wang

2024

J. Appl. Rem. Sens.

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To address the problem of large point cloud data and low segmentation accuracy in indoor and outdoor scenes, we propose a point cloud plane segmentation algorithm based on multiscale region fusion. When using traditional region growing methods for point cloud plane segmentation, the segmentation results are often unstable, and adjacent planes are difficult to separate. In our method, we convert the point cloud data in space into a three-dimensional grid model, and then use the proposed tensor voting method for accurate normal vector estimation. By introducing a multiscale weighted similarity measure to optimize the seed point growing rules, we enhance the accuracy of plane segmentation. Specifically, the algorithm first performs custom grid downsampling and filtering on the original point cloud data. Then different region growing parameters are applied to segment the super-voxel point cloud data at each scale, resulting in multiple segmentation results. The tensor voting is used for estimating the normal vectors of the point cloud, which achieves good estimation performance even at sharp edges. Finally, weights are assigned to similar feature regions, and the similarity measure optimizes the seed point growing rules. With accurate normal vector estimation and appropriate seed point selection, the plane segmentation is more complete. The final segmentation result is obtained by edge preservation and refinement of multiple segmentation results. Experimental results on public and self-collected datasets demonstrate that our proposed method can effectively improve segmentation accuracy while ensuring the real-time performance.

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Section: Related Workmentioning

confidence: 99%

Multiscale region fusion algorithm for 3D plane segmentation

Yang,

Yao,

Wang

2024

J. Appl. Rem. Sens.

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Improving Aerial Targeting Precision: A Study on Point Cloud Semantic Segmentation with Advanced Deep Learning Algorithms

Bozkurt,

Atik,

Duran

2024

Drones

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The integration of technological advancements has significantly impacted artificial intelligence (AI), enhancing the reliability of AI model outputs. This progress has led to the widespread utilization of AI across various sectors, including automotive, robotics, healthcare, space exploration, and defense. Today, air defense operations predominantly rely on laser designation. This process is entirely dependent on the capability and experience of human operators. Considering that UAV systems can have flight durations exceeding 24 h, this process is highly prone to errors due to the human factor. Therefore, the aim of this study is to automate the laser designation process using advanced deep learning algorithms on 3D point clouds obtained from different sources, thereby eliminating operator-related errors. As different data sources, dense 3D point clouds produced with photogrammetric methods containing color information, and point clouds produced with LiDAR systems were identified. The photogrammetric point cloud data were generated from images captured by the Akinci UAV’s multi-axis gimbal camera system within the scope of this study. For the point cloud data obtained from the LiDAR system, the DublinCity LiDAR dataset was used for testing purposes. The segmentation of point cloud data utilized the PointNet++ and RandLA-Net algorithms. Distinct differences were observed between the evaluated algorithms. The RandLA-Net algorithm, relying solely on geometric features, achieved an approximate accuracy of 94%, while integrating color features significantly improved its performance, raising its accuracy to nearly 97%. Similarly, the PointNet++ algorithm, relying solely on geometric features, achieved an accuracy of approximately 94%. Notably, the model developed as a unique contribution in this study involved enriching the PointNet++ algorithm by incorporating color attributes, leading to significant improvements with an approximate accuracy of 96%. The obtained results demonstrate a notable improvement in the PointNet++ algorithm with the proposed approach. Furthermore, it was demonstrated that the methodology proposed in this study can be effectively applied directly to data generated from different sources in aerial scanning systems.

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Grid and homogeneity-based ground segmentation using light detection and ranging three-dimensional point cloud

Cited by 2 publications

References 35 publications

Multiscale region fusion algorithm for 3D plane segmentation

Multiscale region fusion algorithm for 3D plane segmentation

Improving Aerial Targeting Precision: A Study on Point Cloud Semantic Segmentation with Advanced Deep Learning Algorithms

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