Matlab implementation to evaluate RGB-D sensor performance in orchard environments

Introduction

This project is a matlab implementation to evaluate RGB-D sensor performances by analysing RGB-D data acquired in different orchard conditions. The code follows the assessment methodology presented in [1], and it was used to evaluate the performance of Microsoft Kinect v2 by using the KEvOr dataset. Find more information in:

• Assessment methodology to evaluate RGB-D sensors for 3D orchards characterization [1].

Preparation

First of all, create a new project folder:

mkdir new_project

Then, clone the code inside “new_project” folder:

cd new_project
git clone https://github.com/GRAP-UdL-AT/RGBD_sensors_evaluation_in_Orchards.git

Prerequisites

• MATLAB R2020a (we have not tested it in other matlab versions)
• Computer Vision System Toolbox
• Statistics and Machine Learning Toolbox

Data Preparation

Create a folder named “data” inside “new_project” directory.

mkdir data

Inside the “data” folder, save the folder “point_clouds” and file “data_list.csv” available at KEvOr dataset.

Launch the code

• Set the configuration parameters in /new_project/RGBD_sensors_evaluation_in_Orchards/cfg.m (if needed)
• Execute the file /new_project/RGBD_sensors_evaluation_in_Orchards/main.m

Authorship

This project is contributed by GRAP-UdL-AT.

Please contact authors to report bugs @ [email protected]

Citation

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

@article{Gené-Mola2020,
    Author = {{Gen{\'e}-Mola, Jordi and Llorens, Jordi and Rosell-Polo, Joan R and Gregorio, Eduard and Arn{\'o}, Jaume and Solanelles, Francesc and Mart{\'i}nez-Casasnovas, Jos{\'e} A and Escol{\`a}, Alexandre },
    Title = {Assessing the Performance of RGB-D Sensors for 3D Fruit Crop Canopy Characterization under Different Operating and Lighting Conditions },
    Journal = {Sensors},
    Year = {2020}
    doi = {https://doi.org/10.3390/s20247072}
} 

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).