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:
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
- MATLAB R2020a (we have not tested it in other matlab versions)
- Computer Vision System Toolbox
- Statistics and Machine Learning Toolbox
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.
- 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
This project is contributed by GRAP-UdL-AT.
Please contact authors to report bugs @ jordi.genemola@udl.cat
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}
}
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).