Recent advancements in sensor technologies make it possible to collect fine spatial and high temporal resolution remote sensing data and automatically extract informative features in a high throughput mode. As researchers increasingly have access to tools to collect big data, such as Unmanned Aerial Vehicles (UAV) and Controlled Environment Phenotyping Facility (CEPF), there is a need for generating quantitative phenotypic from the collected geospatial data. While precision agriculture technology aims to protect our environment and produce enough food to feed a growing population, the massive volume of geospatial data generated by the research scientists and the lack of software packages customized for processing these data make it challenging to develop transdisciplinary research collaboration around this data. We will share our efforts to develop an open-source online platform for UAS HTP data management to address the big data challenges.
This paper presents a novel method for measuring the size of standard cylinders with the LiDAR and RGB sensors embedded with iPhones. First, we reconstruct 3D points of cylindrical surfaces using the LiDAR data. With 3D point cloud data, we fit the orientation of the cylinder with center pixels. Since the LiDAR does not offer 3D points with high resolution nor high accuracy, we select a segment of the point cloud data and compute the average depth of these segment pixels as the distance from the cylinder to the camera. We then compute the diameter by the geometric relationship of each point on the cylinder. Finally, we improve the measurement accuracy by applying a estimation function. Experimental results show that at distances from 0.3 m to 2 m with different tilt angles, the proposed method can achieve 3 cm cylinder diameter measurement accuracy for the cylinders with a diameter of 8 cm and 14 cm, and 5 cm accuracy for the cylinder with a diameter of 22 cm.
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