This is a MATLAB demo of the Self-Supervised Label Generator (SSLG), presented in our RA-L paper, Self-Supervised Drivable Area and Road Anomaly Segmentation using RGB-D Data for Robotic Wheelchairs. Our SSLG can be used effectively for self-supervised drivable area and road anomaly segmentation based on RGB-D data. The code has been tested in MATLAB R2020b.
We provide five examples in examples, where rgb, depth_u8, depth_u16 and label contain RGB images, normalized depth images, original depth images and segmentation labels, respectively. Run demo.m, and then the generated labels will be presented and saved in examples/output.
These examples belong to our GMRP dataset, an RGB-D dataset of drivable area and road anomaly segmentation for ground mobile robots (e.g., sweeping robots and robotic wheelchairs). Please refer to here for more information.
If you use this code for your research, please cite our paper.
@article{wang2019self,
title = {Self-supervised drivable area and road anomaly segmentation using {RGB-D} data for robotic wheelchairs},
author = {Wang, Hengli and Sun, Yuxiang and Liu, Ming},
journal = {IEEE Robotics and Automation Letters},
volume = {4},
number = {4},
pages = {4386--4393},
year = {2019},
publisher = {IEEE},
doi = {10.1109/LRA.2019.2932874}
}