Conformal Redirected Walking for Shared Indoor Spaces

2019 ◽  
Author(s):  
Yash Tomar ◽  
Ayushi Srivastava ◽  
Arindam Dey ◽  
Ojaswa Sharma
Keyword(s):  
Redirected Walking ◽  
Indoor Spaces

scholarly journals Redirected Walking using Continuous Curvature Manipulation

2021 ◽  
pp. 1-1
Author(s):  
Hiroaki Sakono ◽  
Keigo Matsumoto ◽  
Takuji Narumi ◽  
Hideaki Kuzuoka
Keyword(s):  
Redirected Walking ◽  
Continuous Curvature

scholarly journals LumNet

2021 ◽  
Vol 5 (2) ◽  
pp. 1-20
Author(s):  
Prince U.C. Songwa ◽  
Aaqib Saeed ◽  
Sachin Bhardwaj ◽  
Thijs W. Kruisselbrink ◽  
Tanir Ozcelebi
Keyword(s):  
Control Systems ◽  
Real Life ◽  
Visual Performance ◽  
Lighting Control ◽  
Domain Specific ◽  
Real Life Setting ◽  
Added Benefit ◽  
Measurement Devices ◽  
Indoor Spaces ◽  
Learned Features

High-quality lighting positively influences visual performance in humans. The experienced visual performance can be measured using desktop luminance and hence several lighting control systems have been developed for its quantification. However, the measurement devices that are used to monitor the desktop luminance in existing lighting control systems are obtrusive to the users. As an alternative, ceiling-based luminance projection sensors are being used recently as these are unobtrusive and can capture the direct task area of a user. The positioning of these devices on the ceiling requires to estimate the desktop luminance in the user's vertical visual field, solely using ceiling-based measurements, to better predict the experienced visual performance of the user. For this purpose, we present LUMNET, an approach for estimating desktop luminance with deep models through utilizing supervised and self-supervised learning. Our model learns visual representations from ceiling-based images, which are collected in indoor spaces within the physical vicinity of the user to predict average desktop luminance as experienced in a real-life setting. We also propose a self-supervised contrastive method for pre-training LUMNET with unlabeled data and we demonstrate that the learned features are transferable onto a small labeled dataset which minimizes the requirement of costly data annotations. Likewise, we perform experiments on domain-specific datasets and show that our approach significantly improves over the baseline results from prior methods in estimating luminance, particularly in the low-data regime. LUMNET is an important step towards learning-based technique for luminance estimation and can be used for adaptive lighting control directly on-device thanks to its minimal computational footprint with an added benefit of preserving user's privacy.

scholarly journals Aerial Transmission of the SARS-CoV-2 Virus through Environmental E-Cigarette Aerosols: Implications for Public Policies

2021 ◽  
Vol 18 (4) ◽  
pp. 1437
Author(s):  
Roberto A. Sussman ◽  
Eliana Golberstein ◽  
Riccardo Polosa
Keyword(s):  
Emission Rate ◽  
Respiratory Activity ◽  
Separation Distance ◽  
Mitigation Strategies ◽  
Relative Risks ◽  
Respiratory Flow ◽  
Outdoor Spaces ◽  
Direct Exposure ◽  
Social Separation ◽  
Indoor Spaces

We discuss the implications of possible contagion of COVID-19 through e-cigarette aerosol (ECA) for prevention and mitigation strategies during the current pandemic. This is a relevant issue when millions of vapers (and smokers) must remain under indoor confinement and/or share public outdoor spaces with non-users. The fact that the respiratory flow associated with vaping is visible (as opposed to other respiratory activities) clearly delineates a safety distance of 1–2 m along the exhaled jet to prevent direct exposure. Vaping is a relatively infrequent and intermittent respiratory activity for which we infer a mean emission rate of 79.82 droplets per puff (6–200, standard deviation 74.66) comparable to mouth breathing, it adds into shared indoor spaces (home and restaurant scenarios) a 1% extra risk of indirect COVID-19 contagion with respect to a “control case” of existing unavoidable risk from continuous breathing. As a comparative reference, this added relative risk increases to 44–176% for speaking 6–24 min per hour and 260% for coughing every 2 min. Mechanical ventilation decreases absolute emission levels but keeps the same relative risks. As long as direct exposure to the visible exhaled jet is avoided, wearing of face masks effectively protects bystanders and keeps risk estimates very low. As a consequence, protection from possible COVID-19 contagion through vaping emissions does not require extra interventions besides the standard recommendations to the general population: keeping a social separation distance of 2 m and wearing of face masks.

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

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
João Gaspar Ramôa ◽  
Vasco Lopes ◽  
Luís A. Alexandre ◽  
S. Mogo
Keyword(s):  
Low Power ◽  
Real Time ◽  
Object Classification ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Power Device ◽  
Indoor Environments ◽  
State Classification ◽  
Segmentation Algorithms ◽  
Indoor Spaces

AbstractIn 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.

KR-Net: A Dependable Visual Kidnap Recovery Network for Indoor Spaces

2020 ◽  
Author(s):  
Janghun Hyeon ◽  
Dongwoo Kim ◽  
Bumchul Jang ◽  
Hyunga Choi ◽  
Dong Hoon Yi ◽  
...  
Keyword(s):  
Indoor Spaces

scholarly journals Automatic Reconstruction of Multi-Level Indoor Spaces from Point Cloud and Trajectory

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3493
Author(s):  
Gahyeon Lim ◽  
Nakju Doh
Keyword(s):  
Point Cloud ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Reconstruction Method ◽  
Complex Environments ◽  
Indoor Space ◽  
Multi Level ◽  
Level Building ◽  
Indoor Spaces ◽  
Data Term

Remarkable progress in the development of modeling methods for indoor spaces has been made in recent years with a focus on the reconstruction of complex environments, such as multi-room and multi-level buildings. Existing methods represent indoor structure models as a combination of several sub-spaces, which are constructed by room segmentation or horizontal slicing approach that divide the multi-room or multi-level building environments into several segments. In this study, we propose an automatic reconstruction method of multi-level indoor spaces with unique models, including inter-room and inter-floor connections from point cloud and trajectory. We construct structural points from registered point cloud and extract piece-wise planar segments from the structural points. Then, a three-dimensional space decomposition is conducted and water-tight meshes are generated with energy minimization using graph cut algorithm. The data term of the energy function is expressed as a difference in visibility between each decomposed space and trajectory. The proposed method allows modeling of indoor spaces in complex environments, such as multi-room, room-less, and multi-level buildings. The performance of the proposed approach is evaluated for seven indoor space datasets.

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