Road surface and inventory extraction from mobile LiDAR point cloud using iterative piecewise linear model

Zeybek, Mustafa; Biçici, Serkan

doi:10.1088/1361-6501/acb78d

Cited by 8 publications

(2 citation statements)

References 51 publications

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“…Nonetheless, the scope of generalization inherent to this method exhibits certain limitations, showcasing optimal performance primarily within the context of the trained environment. Instead of learning-based methods, non-learning-based methods [14][15][16][17] can also achieve high performance in ground segmentation. Fischler and Bolles et al [18].…”

Section: Ground Segmentationmentioning

confidence: 99%

A hybrid framework for robust dynamic 3D point clouds removal

Zhu,

Zhang,

Liang

et al. 2023

Meas. Sci. Technol.

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When robot creates a map, dynamic objects can change the space and render the map unusable for navigation. Additionally, the vertical resolution of a VLP-16 LiDAR may be insufficient, making dynamic point removal challenging. To address these challenges, we propose a novel method for dynamic point detection and removal consisting of four components. Firstly, we introduce a multi-resolution heightmap to enhance the efficiency and precision of dynamic point recognition by segmenting ground points. Secondly, we address the issue of limited vertical resolution by fusing multiple scans to simulate additional scan lines and leveraging a multi-resolution range image for precise dynamic point elimination. Thirdly, we apply clustering and principal component analysis-based techniques to compute eigenvectors, facilitating the correction of misclassified static points. Lastly, we propose the utilization of a three-dimensional bounding box strategy to reinforce the monitoring of small static clusters with elevated probabilities of misclassification. These four components complement each other and are executed sequentially. We evaluated our method for both dynamic point removal and ground segmentation on the KITTI dataset and real-world environments. The results demonstrate that our method outperforms baseline methods and generates clean maps.

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Section: Ground Segmentationmentioning

confidence: 99%

A hybrid framework for robust dynamic 3D point clouds removal

Zhu,

Zhang,

Liang

et al. 2023

Meas. Sci. Technol.

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“…The point features in the point cloud are an essential element in the ML algorithm [23,24]. These features capture the characteristics of the environment surrounding each point.…”

Section: Introductionmentioning

confidence: 99%

Different KNN parameters on road surface extraction from UAV-based point cloud under several machine learning algorithms

Biçici

2024

Meas. Sci. Technol.

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Accurate road surface from a three-dimensional (3D) point cloud depends on various parameters. One crucial parameter is the set of point features. Point features enable classification by capturing characteristics of the surface on which the points are located. These features are calculated based on the closest points surrounding each point. In this study, the K-nearest neighbors algorithm (KNN) was applied to identify these closest points. The KNN algorithm requires only one input, the number of closest points (k). Eight different point features were developed using different k values, and their impact on road surface classification from the 3D point cloud was investigated. It was observed that there is no significant improvement in classification accuracy until a certain k value. However, better classification accuracy was achieved after a certain k value. The effect of different k values was also investigated under different training sample structures and machine learning (ML) algorithms. When training samples were selected from a single location as a large group, similar classification accuracy was obtained across different k values. Conversely, when training samples were chosen from various regions in smaller groups rather than a single large group, improved classification was observed as the k value increased. Additionally, it was noted that five different ML algorithms-random forest, support vector machine, generalized linear model, linear discriminant analysis, and robust linear discriminant analysis-have almost similar performance under different k values. Finally, using the optimum k value, improvements of up to 4.543% and 6.601% in accuracy and quality measures, respectively, were found.

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Effectiveness of Different Machine Learning Algorithms in Road Extraction from UAV-Based Point Cloud

Biçici

2024

Lecture Notes in Networks and Systems

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Road surface and inventory extraction from mobile LiDAR point cloud using iterative piecewise linear model

Cited by 8 publications

References 51 publications

A hybrid framework for robust dynamic 3D point clouds removal

A hybrid framework for robust dynamic 3D point clouds removal

Different KNN parameters on road surface extraction from UAV-based point cloud under several machine learning algorithms

Effectiveness of Different Machine Learning Algorithms in Road Extraction from UAV-Based Point Cloud

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