Shared-Autonomy Navigation for Mobile Robots Driven by a Door Detection Module

Beraldo, Gloria; Termine, Enrico; Menegatti, Emanuele

doi:10.1007/978-3-030-35166-3_36

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…Similar algorithms for detecting the doors and door handles have been applied to humanoid robots (Arduengo, Torras, and Sentis, 2019;Llopart, Ravn, and Andersen, 2017). In another study (Beraldo, Termine, and Menegatti, 2019) that uses YOLO, the door positions and center of apertures for navigation of mobile robots were decided, and their statues were determined as open or closed. A depth-based filter was used to reduce false predictions.…”

Section: Learning-based Approachesmentioning

confidence: 99%

Iç Ortamlarda Kapilarin Tespi̇ti̇ İçi̇n Deri̇n Öğrenme Tekni̇kleri̇ni̇n Karşilaştirilmasi

Kaleci

Turgut

2021

Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi

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In indoor environments, the detection of doors (open, semi-opened, and closed) is a crucial task for a variety of fields such as robotics, computer vision, and architecture. The studies that are addressed the door detection problem can be divided into three major categories: 1) closed doors via visual data, 2) open doors via range data, and 3) open, semi-opened, and closed doors via point cloud data. Although some successful studies have been proposed being detected doors via visual and range data under specific circumstances, in this study, we exploited point cloud data due to its ability to describe the 3D characteristic of scenes. The main contribution of this study is two-fold. Firstly, we mainly intended to discover the potential of point-based deep learning architectures such as PointNet, PointNet++, Dynamic Graph Convolutional Neural Network (DGCNN), PointCNN, and Point2Sequence, in contrast to previous studies that generally defined a set of rules depending on the type of door and characteristics of the data. Secondly, the OGUROB DOORS dataset is constructed, which contains point cloud data captured in the GAZEBO simulation environment in different robot positions and orientations. We used precision, recall, and F1-score metrics to analyze the merit and demerit aspects of these architectures. Also, some visual results were given to describe the characteristics of these architectures. The test results showed that all architectures are capable of classifying open, semi-opened, and closed doors over 98% accuracy.

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Section: Learning-based Approachesmentioning

confidence: 99%

Iç Ortamlarda Kapilarin Tespi̇ti̇ İçi̇n Deri̇n Öğrenme Tekni̇kleri̇ni̇n Karşilaştirilmasi

Kaleci

Turgut

2021

Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi

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“…Therefore, the control node is in charge to identify among the NeuroEvent messages the ones communicating the presence of new commands from the user and to translate them into velocity commands according to a rotation angle and the turning time, pre-defined as ROS parameters. Furthermore, to perform more sophisticated robot navigation including the obstacle avoidance, the planning of the trajectory and eventually the use of the environmental information (e.g., a map), the robotic algorithms, already available in ROS such as the navigation stack 6 , can be exploited to develop other ROS nodes in order to achieve more intelligent behaviours of the robotic device [23].…”

Section: B Bci-driven Telepresence Robotmentioning

confidence: 99%