[ { "id": "mortezapoor-2022-photogrammabot", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/78073", "title": "Photogrammabot: An Autonomous ROS-Based Mobile Photography Robot for Precise 3D Reconstruction and Mapping of Large Indoor Spaces for Mixed Reality", "date": "2022-04-20", "abstract": "Precise 3D reconstruction of environments and real objects for Mixed-Reality applications can be burdensome. Photogrammetry can help to create accurate representations of actual objects in the virtual world using a high number of photos of a subject or an environment. Photogrammabot is an affordable mobile robot that facilitates photogrammetry and 3D reconstruction by autonomously and systematically capturing images. It explores an unknown indoor environment and uses map-based localization and navigation to maintain camera direction at different shooting points. Photogrammabot employs a Raspberry Pi 4B and Robot Operating System (ROS) to control the exploration and capturing processes. The photos are taken using a point-and-shoot camera mounted on a 2-DOF micro turret to enable photography from different angles and compensate for possible robot orientation errors to ensure parallel photos. Photogrammabot has been designed as a general solution to facilitate precise 3D reconstruction of unknown environments. In addition we developed tools to integrate it with and extend the Immersive Deck™ MR system [23], where it aids the setup of the system in new locations.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1881, 1731, 5533, 378 ], "booktitle": "Proceedings of 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)", "date_from": "2022-03-12", "date_to": "2022-03-16", "doi": "10.1109/VRW55335.2022.00033", "event": "2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)", "isbn": "978-1-6654-8403-9", "lecturer": [ 1881 ], "location": "Christchurch", "pages": "7", "pages_from": "101", "pages_to": "107", "publisher": "IEEE", "research_areas": [], "keywords": [ "3D reconstruction", "Artificial intelligence", "automation", "autonomous mobile robot", "Computer systems organization", "Computer vision", "Computing methodologies", "Embedded and cyberphysical systems", "fiducial marker tracking", "Human computer interaction (HCI)", "Human computer interaction (HCI)", "Human-centered computing", "Human-centered computing", "Interaction paradigms", "mixed-reality", "Mixed/augmented reality", "Photogrammetry", "Reconstruction", "Robotic autonomy", "Robotics", "ROS", "Virtual reality" ], "weblinks": [], "files": [], "projects_workgroups": [ "d4314" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/mortezapoor-2022-photogrammabot/", "__class": "Publication" }, { "id": "Mossel_Annette_2020-TVR", "type_id": "journalpaper_notalk", "tu_id": 294049, "repositum_id": "20.500.12708/141658", "title": "Immersive training of first responder squad leaders in untethered virtual reality", "date": "2020-12", "abstract": "We present the VROnSite platform that supports immersive training of first responder units´ on-site squad leaders. Our training platform is fully immersive, entirely untethered to ease use and provides two means of navigation-abstract and natural walking-to simulate stress and exhaustion, two important factors for decision making. With the platform´s capabilities, we close a gap in prior art for first responder training. Our research is closely interlocked with stakeholders from multiple fire brigades to gather early feedback in an iterative design process. In this paper, we present the system´s design rationale, provide insight into the process of training scenario development and present results of a user study with 41 squad leaders from the firefighting domain. Virtual disaster environments with two different navigation types were evaluated using quantitative and qualitative measures. Participants considered our platform highly suitable for training of decision making in complex first responder scenarios and results show the importance of the provided navigation technologies in this context.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1815, 1731, 1816, 1727, 1726, 378 ], "doi": "10.1007/s10055-020-00487-x", "journal": "Virtual Reality", "pages_from": "1", "pages_to": "15", "volume": "204", "research_areas": [], "keywords": [ "Virtual Reality", "Mixed Reality", "Augmented Virtuality", "Training", "First Responder", "Interaction", "3D Object Interaction" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2020/Mossel_Annette_2020-TVR/", "__class": "Publication" }, { "id": "Goellner_2019-ABC", "type_id": "inproceedings", "tu_id": 283368, "repositum_id": null, "title": "Virtual Reality CBRN Defence", "date": "2019-10-18", "abstract": "Over the past decade, training in virtual reality for military and disaster preparedness has been increasingly recognized as an important adjunct to traditional modalities of real-life drills. However, there are only a few existing solutions that provide immersive virtual reality training and improve learning through an increased amount of presence. In this paper, we present a novel and flexible Virtual Reality (VR) training system for military and first responders that enables realistic multi-user training in large environments. We show how the requirements of peer stakeholders for disaster relief with an explicit focus on CBRN disaster preparedness transfer to the concept, current implementation and future features of our system. The development and integration of multiple technologies allows a wide variety of interaction and collaboration within our immersive system. In addition, we demonstrate the training capabilities of our proposed system with a multi-user training scenario, simulating a CBRN crisis. Results from our technical and user evaluation with 13 experts in CBRN response from the Austrian Armed Forces (National Defence Academy & Competence Center NBC Defence) indicate strong applicability and user acceptance. Over 80% of the participants agreed “much” or “very much” that the presented system can be used to support training for CBRN-crisis preparedness.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1726, 1727, 1728, 1729, 1731, 378 ], "booktitle": "Meeting Proceedings of the Simulation and Modelling Group Symposium 171", "cfp": { "name": "Call_for_Papers.pdf", "type": "application/pdf", "error": "0", "size": "409042", "orig_name": "Call_for_Papers.pdf", "ext": "pdf" }, "event": "Simulation and Modelling Group Symposium 171", "lecturer": [ 1731, 1726 ], "location": "Vienna", "open_access": "yes", "organization": "NATO S & T", "pages_from": "1", "pages_to": "25", "publisher": "STO", "research_areas": [ "VR" ], "keywords": [ "Virtual Reality", "Mixed Reality", "first responder training", "CBRN", "ABC" ], "weblinks": [ { "href": "https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-MSG-171/MP-MSG-171-16.pdf", "caption": null, "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "Paper", "main_file": false, "use_in_gallery": false, "access": "public", "name": "Goellner_2019-ABC-Paper.pdf", "type": "application/pdf", "size": 797322, "path": "Publication:Goellner_2019-ABC", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/Goellner_2019-ABC/Goellner_2019-ABC-Paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/Goellner_2019-ABC/Goellner_2019-ABC-Paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vr" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2019/Goellner_2019-ABC/", "__class": "Publication" } ]