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Improving the Quality of LiDAR Point Cloud Data for Greenhouse Crop Monitoring

Si, Gaoshoutong
http://rave.ohiolink.edu/etdc/view?acc_num=osu1650111073836686

Abstract Details

2022, Master of Science, Ohio State University, Food, Agricultural and Biological Engineering.
Crop monitoring is of great interest in improving production efficiency, especially in a controlled environment where high-value crops are grown. The advent of small unmanned aerial systems (sUAS) provides an opportunity to acquire high-quality spatial and temporal information for crop monitoring using Light Detection and Ranging (LiDAR) collected point cloud data. However, the point clouds collected using LiDAR can have several limitations, such as occlusion, low point cloud density, outliers, and geometrical distortion, before they can be used effectively for further applications. It is necessary to preprocess the data, so the extracted information from the point cloud is accurate and reliable. It also becomes critical to collect multiple point clouds from different viewing perspectives. Hence, the point clouds need to be stitched to address concerns related to occlusion and low point cloud density. This study addressed the challenges of adapting the Iterative Closest Point (ICP) algorithm for a greenhouse environment application. A pipeline for point cloud registration was established and evaluated to process the LiDAR data collected in a greenhouse. An experiment was conducted in a commercial greenhouse in which point cloud data of crops were collected using a LiDAR mounted on an sUAS. The pipeline identifies the ground floor boundary as a key subset and uses it to improve the initial condition called coarse registration. Then the ICP algorithm is performed to achieve a fine registration. This pipeline was applied to a different combination of point cloud data collected from multiple viewing perspectives. The performance of point cloud registration was evaluated using metrics including visualization, Root of Mean Square Error (RMSE), estimation of the volume of reference objects, and the distribution of point cloud density. This study finds that point cloud registration is affected by several factors, including the overlapped ratio between point clouds, quality of the feature used for registration, and geometric distortion of point clouds. The RMSE value and point cloud density improved after the point cloud registration compared to simply merging two point clouds without registration. Additionally, volume estimation of the reference objects from a registered point cloud is more accurate than a non-registered one. For example, the error of volume estimation decreased from -72.34% to -5.44% after the point cloud registration. This study provides a feasible approach to utilizing the already-existing objects (i.e., ground floor boundary) as the reference for registration and offers insights on how to achieve feature estimation in the greenhouse environment.
Khanal Sami (Advisor)
Peter Ling (Advisor)
Agricultural Engineering
Point cloud registration, sUAS, ICP, Feature extraction

Recommended Citations

Citations

  • Si, G. (2022). Improving the Quality of LiDAR Point Cloud Data for Greenhouse Crop Monitoring [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1650111073836686

    APA Style (7th edition)

  • Si, Gaoshoutong. Improving the Quality of LiDAR Point Cloud Data for Greenhouse Crop Monitoring. 2022. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1650111073836686.

    MLA Style (8th edition)

  • Si, Gaoshoutong. "Improving the Quality of LiDAR Point Cloud Data for Greenhouse Crop Monitoring." Master's thesis, Ohio State University, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=osu1650111073836686

    Chicago Manual of Style (17th edition)

osu1650111073836686
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© 2022, some rights reserved.Creative Commons License Improving the Quality of LiDAR Point Cloud Data for Greenhouse Crop Monitoring by Gaoshoutong Si is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by The Ohio State University and OhioLINK.