Matlab implementation to generate 3D point clouds from data acquired with VLP-16 and GNSS GPS1200+

Introduction

This project is a Matlab implementation to generate 3D point clouds from data acquired with a mobile terrestrial laser scanner (MTLS) comprised of a LiDAR sensor Velodyne VLP-16 (Velodyne LIDAR Inc., San Jose, CA, USA) and a GNSS position sensor GPS1200+ (Leica Geosystems AG, Heerbrugg, Swizeland).

This implementation was used to generate the point clouds provided in LFuji-air dataset, which contains 3D LiDAR data of 11Fuji apple trees with the corresponding fruit position annotations. Find more information in:

Gené-Mola J, Gregorio E, Auat F, Guevara J, Llorens J, Sanz-Cortiella R, Escolà A, Rosell-Polo JR. 2020. LFuji-air dataset: Annotated 3D LiDAR point clouds of Fuji apple trees for fruit detection scanned under different forced air flow conditions. Data in brief, 29 (2020), 105248. DOI: 10.1016/j.dib.2020.105248.

Preparation

First of all, clone the code

git clone https://github.com/GRAP-UdL-AT/MTLS_point_cloud_generation

Place .PCAP files in the data folder /MTLS_point_cloud_generation/test_data. Then convert .PCAP files to .CSV by using Veloview software v3.5.0. This conversion generates a .ZIP file, which should be unziped inside /MTLS_point_cloud_generation/test_data/velodyne_data.

Prerequisites

Matlab 2019b (we have not tested it in other matlab versions)
Veloview 3.5.0

Point cloud generation

Open the file /MTLS_point_cloud_generation/test_data/_dades_preparation_cloud_formation_velodyne.xlsx and set the folder and files names to be processed. Additionally, you can configure some parameters. This parameters depends on the experimental set-up and the scanning conditions, such as the offsets between LiDAR and GNSS sensors.

Open matlab file :/MTLS_point_cloud_generation/cloud_formation_velodyne.m and set the following parameter:

folder_sup = $”data_directory”$;    %folder where file "dades_preparation_cloud_formation_velodyne.xlsx" is placed

example:

folder_sup=['E:\Detecció Fruits 2017\velodyne_vent\code_generacio_nuvols\test_data'];

Execute the file /MTLS_point_cloud_generation/cloud_formation_velodyne.m.

Authorship

This project is contributed by GRAP-UdL-AT.

Please contact authors to report bugs @ j.gene@eagrof.udl.cat

Citation

If you find this implementation or the analysis conducted in our report helpful, please consider citing:

@article{gene2019fruit,
title={LFuji-air dataset: annotated 3D LiDAR point clouds of Fuji apple trees for fruit detection scanned under different forced air flow conditions.},
author={Gen{\'e}-Mola, Jordi and Gregorio, Eduard and Cheein, Fernando Auat and Guevara, Javier and Llorens, Jordi and Sanz-Cortiella, Ricardo and Escol{\`a}, Alexandre and Rosell-Polo, Joan R},
journal={Data in Brief},
volume={29},
pages={105248},
year={2020},
publisher={Elsevier}
}

@article{gene2019fruit,
title={Fruit detection in an apple orchard using a mobile terrestrial laser scanner},
author={Gen{\'e}-Mola, Jordi and Gregorio, Eduard and Guevara, Javier and Auat, Fernando and Sanz-Cortiella, Ricardo and Escol{\`a}, Alexandre and Llorens, Jordi and Morros, Josep-Ramon and Ruiz-Hidalgo, Javier and Vilaplana, Ver{\'o}nica and others},
journal={Biosystems engineering},
volume={187},
pages={171--184},
year={2019},
publisher={Elsevier}
}

@article{gene2020fruit,
title={Fruit detection, yield prediction and canopy geometric characterization using LiDAR with forced air flow},
author={Gen{\'e}-Mola, Jordi and Gregorio, Eduard and Cheein, Fernando Auat and Guevara, Javier and Llorens, Jordi and Sanz-Cortiella, Ricardo and Escol{\`a}, Alexandre and Rosell-Polo, Joan R},
journal={Computers and Electronics in Agriculture},
volume={168},
pages={105121},
year={2020},
publisher={Elsevier}
}

Acknowledgements

This work was partly funded by the Spanish Ministry of Science, Innovation and Universities (grant RTI2018-094222-B-I00[PAgFRUIT project] by MCIN/AEI/10.13039/501100011033 and by “ERDF, a way of making Europe”, by the European Union).