A Deep-Learning-based System for Indoor Active Cleaning

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.

Authors

• Yike Yun
• Linjie Hou
• Zijian Feng
• Wei Jin
• Yang Liu
• Heng Wang
• Ruonan He
• Weitao Guo
• Bo Han
• Baoxing Qin
• Jiaxin Li

Keywords

• Measurement
• Location awareness
• Solid modeling
• Liquids
• Solids
• Cleaning
• Planning
• Solid Waste
• Clear Strategy
• Perception Module
• Lifespan
• Convolutional Neural Network
• Object Detection
• Data Augmentation
• Precision And Recall
• Bounding Box
• Semantic Features
• Tracking Algorithm
• Largest Dataset
• Planning Control
• QR Code
• Image Coordinates
• Random Flipping
• Test Videos
• Distance Interval
• Robot Operating System
• Ground-truth Box
• Homography Matrix
• World Frame