Estimating Door Shape and Manipulation Model for Daily Assistive Robots Based on the Integration of Visual and Touch Information

Nagahama, Kotaro; Takeshita, Keisuke; Yaguchi, Hiroaki; Yamazaki, Kimitoshi; Yamamoto, Takashi; Inaba, Masayuki

doi:10.1109/iros.2018.8593391

Cited by 10 publications

(6 citation statements)

References 17 publications

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“…Additionally, it can be observed that these algorithms are executed in real time. Regarding hardware characteristics, robotic platforms such as the following robots were used: PR2 [ 105 ], Toyota’s Human Support [ 107 ], Romeo 2 [ 106 ], Turtlebot 2 [ 98 ], and NAO [ 108 ].…”

Section: Discussion and Conclusionmentioning

confidence: 99%

“…Finally, the panoramic object representation for accurate model attributing (PANORAMA) algorithm was used to classify the 3D objects. Nevertheless, Nagahama et al in [ 107 ] developed the shape manipulation model estimator (SVE-V) algorithm for Toyota’s human support robot to recognize and open a door in domestic environments. The authors used an RGB-D sensor to extract a point cloud and calculate all planes perpendicular to the floor surface.…”

Section: Algorithms Used For Objectsmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Montaño-Serrano

Jacinto‐Villegas

Vilchis-González

et al. 2021

Sensors

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Today, computer vision algorithms are very important for different fields and applications, such as closed-circuit television security, health status monitoring, and recognizing a specific person or object and robotics. Regarding this topic, the present paper deals with a recent review of the literature on computer vision algorithms (recognition and tracking of faces, bodies, and objects) oriented towards socially assistive robot applications. The performance, frames per second (FPS) processing speed, and hardware implemented to run the algorithms are highlighted by comparing the available solutions. Moreover, this paper provides general information for researchers interested in knowing which vision algorithms are available, enabling them to select the one that is most suitable to include in their robotic system applications.

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Section: Discussion and Conclusionmentioning

confidence: 99%

Section: Algorithms Used For Objectsmentioning

confidence: 99%

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Montaño-Serrano

Jacinto‐Villegas

Vilchis-González

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Other applications using service robots include object manipulation; for example, in [4] a robot starts their motion planing system using previous experience in consistent situations. Similarly, in [5] and [6], the authors present a system where a service robot is able to manipulate unknown doors by receiving a single users instruction. Although those system presented high performance, the limitation of the application and the necessity of user feedback make them hard to extend those systems to general purpose service robots.…”

Section: Related Workmentioning

confidence: 99%

sDSPL - Towards a benchmark for general-purpose task evaluation in domestic service robots

Contreras¹,

Matsusaka²,

Yamamoto³

et al. 2021

Preprint

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Although the skills required to solve isolated robotics problems are reaching amazing performances recently, we propose the evaluation of such individual solutions in fully integrated robot systems tested in real daily situations like those presented at international robotics competitions. The simulation Domestic Standard Platform League (sDSPL), which utilizes the HSR simulator developed for the World Robot Summit, surges from the necessity to standardise and spread the research on Domestic Service Robots where a series of solutions can be tested to solve a general-purpose task in a standard domestic environment; this approach has been proven successful at several international competitions, namely, the RoboCup Japan Open, the Mexican Tournament of Robotics, and the RoboCup 2021.<div><br></div><div>**This work was accepted to The 39th Annual Conference of the Robotics Society of Japan.<br></div>

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“…Lastly, a confidence value was estimated while considering ideal and calculated door dimensions in terms of difference and standard deviation. A similar work detected closed doors presented by Nagahama, Takeshita, Yaguchi, Yamazaki, Yamamoto, and Inaba (2018). They first passed the point cloud data from the bilateral filter, and they calculated the normals of points.…”

Section: Kakillioglu Et Al Transferred Wall Planes Into Depthmentioning

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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Estimating Door Shape and Manipulation Model for Daily Assistive Robots Based on the Integration of Visual and Touch Information

Cited by 10 publications

References 17 publications

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

sDSPL - Towards a benchmark for general-purpose task evaluation in domestic service robots

Iç Ortamlarda Kapilarin Tespi̇ti̇ İçi̇n Deri̇n Öğrenme Tekni̇kleri̇ni̇n Karşilaştirilmasi

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