Navigating visually impaired travelers in a large train station using smartphone and bluetooth low energy

Kim, Jee‐Eun; Bessho, Masahiro; Kobayashi, Shinsuke; Koshizuka, Noboru; Satoh, Ken

doi:10.1145/2851613.2851716

Cited by 78 publications

(29 citation statements)

References 19 publications

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“…Currently, the research community of assistive technology tends to solve localization issues in indoor scenarios. Meanwhile, most of the approaches are based on beacon-based navigation systems [31], [32], where the hardware maintenance at the specific places, even if the maintenance cost can be controlled, limits the application range of navigation systems. In our previous work, we proposed a series of visual localization approaches in the field of assistive navigation.…”

Section: B (Assistive) Visual Localizationmentioning

confidence: 99%

Unifying Visual Localization and Scene Recognition for People With Visual Impairment

et al. 2020

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With the development of computer vision and mobile computing, assistive navigation for people with visual impairment arouses the attention of research communities. As two key challenges of assistive navigation, ''Where am I?'' and ''What are the surroundings?'' are still to be resolved by taking advantage of visual information. In this paper, we leverage the prevailing compact network as the backbone to build a unified network featuring two branches that implement scene description and scene recognition separately. Based on the unified network, the proposed pipeline performs scene recognition and visual localization simultaneously in the scenario of assistive navigation. The visual localization pipeline involves image retrieval and sequence matching. In the experiments, different configurations of the proposed pipeline are tested on public datasets to search for the optimal parameters. Moreover, on the real-world datasets captured by the wearable assistive device, the proposed assistive navigation pipeline is proved to achieve satisfactory performance. On the challenging dataset, the top-5 precision of scene recognition is more than 80%, and the visual localization precision is over 60% under a recall of 60%. The related codes and datasets are open-source online at https://github.com/chengricky/ScenePlaceRecognition.

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Section: B (Assistive) Visual Localizationmentioning

confidence: 99%

Unifying Visual Localization and Scene Recognition for People With Visual Impairment

et al. 2020

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“…Prior research investigated various indoor navigation approaches to support people with visual impairments [35,36]. Among these, smartphone-based turn-by-turn navigation systems using BLE beacons provide accurate navigation assistance, feature an off-the-shelf infrastructure, and are easy to deploy [37][38][39][40][41][42]. Turn-by-turn navigation is a guidance method that orients a user towards a destination through sequential vocal messages.…”

Section: Navigation Assistance For People With Visual Impairmentsmentioning

confidence: 99%

Smartphone-based localization for blind navigation in building-scale indoor environments

Masayuki

Ahmetovic

Sato

et al. 2019

Pervasive and Mobile Computing

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Continuous, accurate, and real-time smartphone-based localization is a promising technology for supporting independent mobility of people with visual impairments. However, despite extensive research on indoor localization techniques, localization technologies are still not ready for deployment in large and complex environments such as shopping malls and hospitals, where navigation assistance is needed most. We identify six key challenges for accurate smartphone localization related to the large-scale nature of the navigation environments and the user's mobility. To address these challenges, we present a series of techniques that enhance a probabilistic localization algorithm. The algorithm utilizes mobile device inertial sensors and Received Signal Strength (RSS) from Bluetooth Low Energy (BLE) beacons. We evaluate the proposed system in a 21,000 m 2 shopping mall that includes three multi-story buildings and a large open underground passageway. Experiments conducted in this environment demonstrate the effectiveness of the proposed technologies to improve localization accuracy. Field experiments with visually impaired participants confirm the practical performance of the proposed system in realistic use cases.

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“…In the research field of assisted navigation, localization systems aim to locate the user by exploiting different types of sensors. Beacon-based localization approaches locate the user by the proximity to a beacon, which could be Bluetooth beacons [ 10 ], RFID beacons [ 11 ], etc. Beacon-based localization is usually applied to indoor environments, and is not very suitable for outdoor localization.…”

Section: Related Workmentioning

confidence: 99%

Visual Localizer: Outdoor Localization Based on ConvNet Descriptor and Global Optimization for Visually Impaired Pedestrians

Lin

Cheng

Wang

et al. 2018

Sensors

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Localization systems play an important role in assisted navigation. Precise localization renders visually impaired people aware of ambient environments and prevents them from coming across potential hazards. The majority of visual localization algorithms, which are applied to autonomous vehicles, are not adaptable completely to the scenarios of assisted navigation. Those vehicle-based approaches are vulnerable to viewpoint, appearance and route changes (between database and query images) caused by wearable cameras of assistive devices. Facing these practical challenges, we propose Visual Localizer, which is composed of ConvNet descriptor and global optimization, to achieve robust visual localization for assisted navigation. The performance of five prevailing ConvNets are comprehensively compared, and GoogLeNet is found to feature the best performance on environmental invariance. By concatenating two compressed convolutional layers of GoogLeNet, we use only thousands of bytes to represent image efficiently. To further improve the robustness of image matching, we utilize the network flow model as a global optimization of image matching. The extensive experiments using images captured by visually impaired volunteers illustrate that the system performs well in the context of assisted navigation.

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Navigating visually impaired travelers in a large train station using smartphone and bluetooth low energy

Cited by 78 publications

References 19 publications

Unifying Visual Localization and Scene Recognition for People With Visual Impairment

Unifying Visual Localization and Scene Recognition for People With Visual Impairment

Smartphone-based localization for blind navigation in building-scale indoor environments

Visual Localizer: Outdoor Localization Based on ConvNet Descriptor and Global Optimization for Visually Impaired Pedestrians

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