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基于骨架點的矮化密植棗樹三維點云自動配準
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國家自然科學基金項目(31870347)


Three-dimensional Point Cloud Automatic Registration for Dwarf and Dense Planted Jujube Tree Based on Skeleton Points
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    摘要:

    為了實現(xiàn)棗園的自動化管理,,針對棗樹自動化選擇性冬剪作業(yè)要求,,需要重建出矮化密植無葉棗樹枝干的三維模型。利用2臺固定的Azure Kinect DK深度相機搭建獲取棗樹點云信息的三維重建系統(tǒng)平臺,,然后把系統(tǒng)平臺逆時針旋轉(zhuǎn)55°獲取同一棵棗樹的另一幀三維點云信息,。為了自動完成2幀點云的配準,提出了基于骨架點的棗樹點云配準方法:首先利用FPFH特征描述子計算骨架點的特征向量,,并采用SAC-IA(采樣一致性)算法對2個視角下的棗樹骨架點云進行初始匹配,;其次利用經(jīng)典的ICP算法對初始位姿進行優(yōu)化;最終只采用2個視角下的點云重建棗樹枝干的三維模型,。實驗對比了在3種典型自然環(huán)境下(晴天,、陰天、夜間)棗樹點云的配準精度和配準時間,,結(jié)果表明:晴天時對采集系統(tǒng)有一定的影響,,使得配準后的棗樹枝干有部分不完整;陰天和夜間對采集系統(tǒng)影響小,,能夠重建出完整的棗樹枝干,;相對于陰天和夜間,晴天時,,棗樹點云配準耗時最少,,為0.09s,而配準誤差最大,,其擬合分數(shù)為0.00029,;陰天時,棗樹點云配準時間介于晴天和夜間之間,,為0.12s,,而此時配準誤差最小,其擬合分數(shù)為0.00011;夜間配準誤差介于晴天和陰天,,且此時配準時間最長,,為0.16s。

    Abstract:

    The current planting pattern of dwarf and dense jujube tree was increasingly conducive to mechanized harvesting, spraying and automated pruning, in which pruning of jujube tree removed redundant or overgrown branches and controlled tree structure to increase yield and extend life cycle. There were two main pruning methods: artificial pruning and whole geometric pruning. The quality of artificial pruning which was low efficiency, high labor cost and labor intensity was high. Higher efficiency can be achieved through whole geometric pruning with pruning machine adopted fixed distance, but wrong and missing pruning was serious. Automatic selective pruning reduced cost of labor, with a large number of useful branches being protected. In order to meet the requirement of automatic selective pruning of jujube tree, a complete 3D model of tree was needed. To ensure that two frames of point clouds of two fixed cameras were matched together with high accuracy, point cloud registration revisited algorithm was used. A system platform was built with two Azure Kinect DK depth cameras to obtain two fames of point clouds of a same tree in two positions with 55 degrees rotation difference. In order to register these two frames automatically, skeleton points based registration pipeline was proposed. Firstly, skeleton points’eigenvectors were calculated with fast point feature histograms (FPFH). Then, sample consensus initial alignment (SAC-IA) was applied for rough registering. Finally, fine registration with ICP algorithm was done with KD-tree acceleration to obtain a completed 3D branches model. The registration precision and time for the different natural environments were compared in the experiment, and results showed that illumination had a significant influence on the number of point clouds of jujube tree collected by the system. As a result, jujube branches 3D model were partially incomplete in sunny days, while complete jujube branches can be reconstructed in cloudy days and at night. In sunny days, the registration time was the minimum, only 0.09s, and error was the largest with fitting score of 0.00029;in cloudy day the registration time was 0.12s between sunny days and night, and the error was the smallest with fitting score of 0.00011. Compared with sunny and cloudy days, registration time was the longest at 0.16s at night, and the error was between sunny and cloudy days.

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馬保建,鄢金山,王樂,蔣煥煜.基于骨架點的矮化密植棗樹三維點云自動配準[J].農(nóng)業(yè)機械學報,2021,52(9):24-32. MA Baojian, YAN Jinshan, WANG Le, JIANG Huanyu. Three-dimensional Point Cloud Automatic Registration for Dwarf and Dense Planted Jujube Tree Based on Skeleton Points[J]. Transactions of the Chinese Society for Agricultural Machinery,2021,52(9):24-32.

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  • 收稿日期:2020-10-14
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  • 在線發(fā)布日期: 2021-09-10
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