A Deep-Learning-based System for Indoor Active Cleaning
Yike Yun, Linjie Hou, Zijian Feng, Wei Jin, Yang Liu, Heng Wang, Ruonan He, Weitao Guo
Abstract
Cleaning public areas like commercial complexes is challenging due to their sophisticated surroundings and the vast kinds of real-life dirt. Robots are required to distinguish dirts and apply corresponding cleaning strategies. In this work, we proposed an active-cleaning framework by utilizing deep-learning methods for both solid wastes detection and liquid stains segmentation. Our system consists of 4 components: a Perception module integrated with deep-learning models, a Post-processing module for projection, a Tracking module for map localization, and a Planning and Control module for cleaning strategies. Compared with classic approaches, our vision-based system significantly improves cleaning efficiency. Besides, we released the largest real-world indoor hybrid dirt cleaning dataset (HD10K) containing 10K labeled images, together with a track-level evaluation metric for better cleaning performance measurement. The proposed deep-learning based system is verified with extensive experiments on our dataset, and deployed to Gaussian Robotics's robots operating globally. Dataset is available at: https://gaussianopensource.github.io/projects/active_cleaning.
BibTeX
@inproceedings{iros2022_adeeplearningbas,
title = {A Deep-Learning-based System for Indoor Active Cleaning},
author = {Yike Yun and Linjie Hou and Zijian Feng and Wei Jin and Yang Liu and Heng Wang and Ruonan He and Weitao Guo and Bo Han and Baoxing Qin and Jiaxin Li},
booktitle = {IROS 2022},
year = {2022}
}