Door and cabinet recognition using Convolutional Neural Nets and real-time method fusion for handle detection and grasping

Llopart, Adrian; Ravn, Ole; Andersen, Nils Axel

doi:10.1109/iccar.2017.7942676

Cited by 24 publications

(28 citation statements)

References 11 publications

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“…There are already a vast number of studies that used door detection and classification for robot navigation tasks as moving between rooms, robotic handle grasping and others. Some have used sonar sensors with visual information, [7,8], others used only colour and shape information, [9], or just 3D shape information, [10], some have used simple feature extractors, [11,12] and others have used more modern methods like CNN (convolutional neural networks), [13] and the use of 3D information, [14][15][16][17][18][19].…”

Section: Related Workmentioning

confidence: 99%

Real-time 2D–3D door detection and state classification on a low-power device

et al. 2021

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In this paper, we propose three methods for door state classification with the goal to improve robot navigation in indoor spaces. These methods were also developed to be used in other areas and applications since they are not limited to door detection as other related works are. Our methods work offline, in low-powered computers as the Jetson Nano, in real-time with the ability to differentiate between open, closed and semi-open doors. We use the 3D object classification, PointNet, real-time semantic segmentation algorithms such as, FastFCN, FC-HarDNet, SegNet and BiSeNet, the object detection algorithm, DetectNet and 2D object classification networks, AlexNet and GoogleNet. We built a 3D and RGB door dataset with images from several indoor environments using a 3D Realsense camera D435. This dataset is freely available online. All methods are analysed taking into account their accuracy and the speed of the algorithm in a low powered computer. We conclude that it is possible to have a door classification algorithm running in real-time on a low-power device.

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Section: Related Workmentioning

confidence: 99%

Real-time 2D–3D door detection and state classification on a low-power device

et al. 2021

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“…In [16], the authors also applied a CNN to detect doors and grasp them with the help of a mobile robot. After the detection of doors and handles, the combination of two different methods assured the identification of the point cloud of the handle within the Region Of Interest (ROI).…”

Section: Related Workmentioning

confidence: 99%

Comparison between digital image processing and learning techniques on a door detection task

Kappel¹,

Ferreira²

2020

Proceedings of the III Brazilian Humanoid Robot Workshop (BRAHUR) and IV Brazilian Workshop on Service Robotics (BRASERO)

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Door detection is a core task for any indoor autonomous robot at any level, such as assistive and domestic robots. However, the application of machine learning techniques can face hard constraints for embedded devices or autonomous platforms such as processing, memory, and energy. This paper aims to analyze and discuss the viability of two different approaches to the problem, a straight Digital Image Processing technique, and a Machine Learning based technique. Both strategies got compared under an Nvidia Jetson Nano. Therefore, after analyzing the resultant data, the Machine Learning based technique has shown the best overall mAP when compared to the Digital Image Processing technique (0.45 and 0.21, respectively). Also, the Machine Learning approach has proven to be more stable time-wise, which is another positive feature.

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“…In another case study, a door and cabinet recognition system was proposed by Maurin et al for a mobile robot. The author used a 7 ×7 darknet deep-learning model to recognize the door and k-means color clustering for segmentation of the handle point [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

A Human Support Robot for the Cleaning and Maintenance of Door Handles Using a Deep-Learning Framework

Ramalingam

Yin

Elara

et al. 2020

Sensors

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The role of mobile robots for cleaning and sanitation purposes is increasing worldwide. Disinfection and hygiene are two integral parts of any safe indoor environment, and these factors become more critical in COVID-19-like pandemic situations. Door handles are highly sensitive contact points that are prone to be contamination. Automation of the door-handle cleaning task is not only important for ensuring safety, but also to improve efficiency. This work proposes an AI-enabled framework for automating cleaning tasks through a Human Support Robot (HSR). The overall cleaning process involves mobile base motion, door-handle detection, and control of the HSR manipulator for the completion of the cleaning tasks. The detection part exploits a deep-learning technique to classify the image space, and provides a set of coordinates for the robot. The cooperative control between the spraying and wiping is developed in the Robotic Operating System. The control module uses the information obtained from the detection module to generate a task/operational space for the robot, along with evaluating the desired position to actuate the manipulators. The complete strategy is validated through numerical simulations, and experiments on a Toyota HSR platform.

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Door and cabinet recognition using Convolutional Neural Nets and real-time method fusion for handle detection and grasping

Cited by 24 publications

References 11 publications

Real-time 2D–3D door detection and state classification on a low-power device

Real-time 2D–3D door detection and state classification on a low-power device

Comparison between digital image processing and learning techniques on a door detection task

A Human Support Robot for the Cleaning and Maintenance of Door Handles Using a Deep-Learning Framework

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