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Sensors 2018, 18(9), 3014; https://doi.org/10.3390/s18093014

Non-Contact Body Measurement for Qinchuan Cattle with LiDAR Sensor

1
College of Information Engineering, Northwest A&F University, Yangling, Xianyang 712100, China
2
Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Xianyang 712100, China
3
School of Information Management, Wuhan University, Wuhan 430072, China
4
College of Computer Science, Wuhan University, Wuhan 430072, China
5
Western E-commerce Co., Ltd., Yinchuan 750004, China
*
Authors to whom correspondence should be addressed.
Received: 19 August 2018 / Revised: 2 September 2018 / Accepted: 5 September 2018 / Published: 9 September 2018
(This article belongs to the Special Issue Sensors in Agriculture 2018)
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Abstract

The body dimension measurement of large animals plays a significant role in quality improvement and genetic breeding, and the non-contact measurements by computer vision-based remote sensing could represent great progress in the case of dangerous stress responses and time-costing manual measurements. This paper presents a novel approach for three-dimensional digital modeling of live adult Qinchuan cattle for body size measurement. On the basis of capturing the original point data series of live cattle by a Light Detection and Ranging (LiDAR) sensor, the conditional, statistical outliers and voxel grid filtering methods are fused to cancel the background and outliers. After the segmentation of K-means clustering extraction and the RANdom SAmple Consensus (RANSAC) algorithm, the Fast Point Feature Histogram (FPFH) is put forward to get the cattle data automatically. The cattle surface is reconstructed to get the 3D cattle model using fast Iterative Closest Point (ICP) matching with Bi-directional Random K-D Trees and a Greedy Projection Triangulation (GPT) reconstruction method by which the feature points of cattle silhouettes could be clicked and calculated. Finally, the five body parameters (withers height, chest depth, back height, body length, and waist height) are measured in the field and verified within an accuracy of 2 mm and an error close to 2%. The experimental results show that this approach could be considered as a new feasible method towards the non-contact body measurement for large physique livestock. View Full-Text
Keywords: body dimensions; filtering; clustering; RANSAC; segmentation; ICP matching; reconstruction; FPFH body dimensions; filtering; clustering; RANSAC; segmentation; ICP matching; reconstruction; FPFH
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Huang, L.; Li, S.; Zhu, A.; Fan, X.; Zhang, C.; Wang, H. Non-Contact Body Measurement for Qinchuan Cattle with LiDAR Sensor. Sensors 2018, 18, 3014.

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