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:

• LFuji-air dataset: annotated 3D LiDAR point clouds of Fuji apple trees for fruit detection scanned under different forced air flow conditions.. (submitted, not publicly available yet).

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={Submitted},
}

@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).