Fast plane extraction method based on the point pair feature

Xiao, Zhengtao; Gao, Jian; Wu, Dongqing; Zhang, Lanyu; Zhang, Qiaofen

doi:10.1007/s11042-022-14063-9

Cited by 1 publication

(2 citation statements)

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“…In order to lessen the consumption of computing resources, uniform downsampling [25] is used to downsample the original point cloud S. The downsampled point cloud is labeled as S 1 and is subsequently utilized to apply an edge extraction algorithm [26], which is used to identify the boundary point set Ω 1 (see Figure 6). Next, the Euclidean distance C pi between adjacent points in a clockwise direction along the set Ω 1 is calculated.…”

Section: Leaf Perimetermentioning

confidence: 99%

“…As described in Section 2.2, the images were captured using a stereo camera, followed by filtering and stereo matching to obtain the original point cloud. Subsequently, the point clouds preprocessed at different periods, as illustrated in Figure 10, and underwent ICP registration [23] and uniform downsampling [25] with a sampling radius of 20. The respective point cloud numbers were 53,487, 47,356, and 125,690.…”

Section: Point Cloud Acquisitionmentioning

confidence: 99%

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Crop Leaf Phenotypic Parameter Measurement Based on the RKM-D Point Cloud Method

Mu,

Li,

Deng

et al. 2024

Sensors

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Crop leaf length, perimeter, and area serve as vital phenotypic indicators of crop growth status, the measurement of which is important for crop monitoring and yield estimation. However, processing a leaf point cloud is often challenging due to cluttered, fluctuating, and uncertain points, which culminate in inaccurate measurements of leaf phenotypic parameters. To tackle this issue, the RKM-D point cloud method for measuring leaf phenotypic parameters is proposed, which is based on the fusion of improved Random Sample Consensus with a ground point removal (R) algorithm, the K-means clustering (K) algorithm, the Moving Least Squares (M) method, and the Euclidean distance (D) algorithm. Pepper leaves were obtained from three growth periods on the 14th, 28th, and 42nd days as experimental subjects, and a stereo camera was employed to capture point clouds. The experimental results reveal that the RKM-D point cloud method delivers high precision in measuring leaf phenotypic parameters. (i) For leaf length, the coefficient of determination (R2) surpasses 0.81, the mean absolute error (MAE) is less than 3.50 mm, the mean relative error (MRE) is less than 5.93%, and the root mean square error (RMSE) is less than 3.73 mm. (ii) For leaf perimeter, the R2 surpasses 0.82, the MAE is less than 7.30 mm, the MRE is less than 4.50%, and the RMSE is less than 8.37 mm. (iii) For leaf area, the R2 surpasses 0.97, the MAE is less than 64.66 mm2, the MRE is less than 4.96%, and the RMSE is less than 73.06 mm2. The results show that the proposed RKM-D point cloud method offers a robust solution for the precise measurement of crop leaf phenotypic parameters.

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Section: Leaf Perimetermentioning

confidence: 99%

Section: Point Cloud Acquisitionmentioning

confidence: 99%