SeeNav

Noreikis, Marius; Xiao, Yu; Ylä-Jääski, Antti

doi:10.1145/3126686.3126733

Cited by 19 publications

(12 citation statements)

References 19 publications

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“…It may happen that the location is inside an actual undiscovered obstacle. 2) the user reaches task location using our indoor positioning system that has up to 1 meter positioning error [14]. However, the offset does not notably influence the final result.…”

Section: B Datasetsmentioning

confidence: 95%

“…https://photographylife.com/what-is-exif-data number of 3D points in the model. SfM-based 3D models have also been used for implementing image-based localization [13] and augmented reality based navigation [14].…”

Section: A Structure From Motionmentioning

confidence: 99%

“…If the participant confirms the task, the mobile client will receive navigation instructions from the backend server, and will guide the participant to the destination in an Augmented Reality (AR) mode ( Figure 3b). We utilize our previous work for indoor positioning and navigation [14].…”

Section: System Overviewmentioning

confidence: 99%

“…The localization is implemented based on image feature matching. We reuse the AR navigation functionality developed in our previous work [14]. The module is responsible for providing 2D maps for a client application, serving localization queries and making sure that a client can quickly and easily reach an assigned task location.…”

Section: System Overviewmentioning

confidence: 99%

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SnapTask: Towards Efficient Visual Crowdsourcing for Indoor Mapping

Noreikis

Xiao

et al. 2018

2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS)

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Visual crowdsourcing (VCS) offers an inexpensive method to collect visual data for implementing tasks, such as 3D mapping and place detection, thanks to the prevalence of smartphone cameras. However, without proper guidance, participants may not always collect data from desired locations with a required Quality-of-Information (QoI). This often causes either a lack of data in certain areas, or extra overheads for processing unnecessary redundancy. In this work, we propose SnapTask, a participatory VCS system that aims at creating complete indoor maps by guiding participants to efficiently collect visual data of high QoI. It applies Structure-from-Motion (SfM) techniques to reconstruct 3D models of indoor environments, which are then converted into indoor maps. To increase coverage with minimal redundancy, SnapTask determines locations for the next data collection tasks by analyzing the coverage of the generated 3D model and the camera views of the collected images. In addition, it overcomes the limitations of SfM techniques by utilizing crowdsourced annotations to reconstruct featureless surfaces (e.g. glass walls) in the 3D model. According to a field test in a library, the indoor map generated by SnapTask successfully reconstructs 100% of the library walls and 98.12% of objects and traversal areas within the library. With the same amount of input data our design of guided data collection increases the map coverage by 20.72% and 34.45%, respectively, compared with unguided participatory and opportunistic VCS.

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Section: B Datasetsmentioning

confidence: 95%

Section: A Structure From Motionmentioning

confidence: 99%

Section: System Overviewmentioning

confidence: 99%

Section: System Overviewmentioning

confidence: 99%

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SnapTask: Towards Efficient Visual Crowdsourcing for Indoor Mapping

Noreikis

Xiao

et al. 2018

2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS)

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“…For wayfinding in an indoor environment, an indoor navigation solution such as SeeNav [28] provides navigation using an augmented reality application on a mobile device. Implementing an augmented reality indoor navigation application using services such as Mapbox [2] together with tools such as Unity [3] is also a possibility as demonstrated by Pavani [30] in an online tutorial.…”

Section: Related Workmentioning

confidence: 99%

Space Pace: Method for Creating Augmented Reality Tours Based on 360 Videos

Nummenmaa

Buruk

Bujić

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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In this paper, guidelines are presented for creating videobased guided tours that employ 360°video content and produce the feeling of augmented reality. The benefit of the approach presented in this paper is that it does not rely on heavy technological requirements but can be implemented by anyone with a consumer level camera capable of making 360°video recordings, in a variety of locations with low cost and modest technological prowess. Principle application areas are for example museum and city tours, wayfinding applications and crafted narrative experiences. The guidelines were derived via a pilot implementation of the tour experience, which was initially ideated using workshop methods. The evaluation of the pilot showed that the approach is promising as a new way to experience locations, and provides us with guidelines that can be classified as essential, recommended and needing consideration for developing and applied such technology. Our guidelines describe and specify a novel method of creating 360°video recordings using low-cost and readily available hardware. The method can be employed by a wide variety of actors to create services administering AR-like experiences in a cost and time effective manner. Keywords: Augmented reality • 360°video • Mobile augmented reality • Guidelines • Design • Guided tour • Location-based services • Wayfinding • Navigational aids • Spatial cognition

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