A fast image matching technique for the panoramic-based localization

Huang, Jiung-Yao; Lee, Su-Hui; Tsai, Chien‐Hsiung

doi:10.1109/icis.2016.7550792

Cited by 13 publications

(11 citation statements)

References 15 publications

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“…Huang et al [13] proposed a Sorting Hat method to filter out unrelated element pairs to accommodate the mobile device environment where the image matching process needs to achieve a good design meeting the performance requirements. Sorting Hat starts the matching process from the innermost ring (containing the most critical features to determine similarity between two images) toward the outer ring.…”

Section: Molnar and Kovacsmentioning

confidence: 99%

An Automatic Indoor Positioning Robot System Using Panorama Feature Matching

Chang¹,

Chen²,

Kambhampaty³

et al. 2021

International Conferences on Distributed Multimedia Systems

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With Location-Based Service (LBS) support, a mobile user can enjoy specific information/services based on their location. The GPS-based positioning technique plays an essential role in outdoor LBS applications. Owing to physical barriers and potential interference inside a building, GPS-based positioning systems do not work well for commercial indoor LBS applications. Various wireless technology-based position systems have been proposed to solve indoor positioning problems (e.g., Wi-Fi, Bluetooth, infrared, and RFID), but they come with challenges involving accuracy, installation, interference, and power consumption. We propose a next generation indoor localization recognition system called IP-PFM (Indoor Position based on Panorama Feature Matching) to overcome the limitations and problems of wireless technology-based approaches. By applying panorama image recognition technology, IP-PFM accurately pinpoints the indoor position. Further, IP-PFM also includes an indoor position system chat bot feature to automate chat in real-time with users -using the Line Bot application programming interface (API)-which allows a mobile user to post a picture taken with their smartphone. Upon receiving the picture, IP-PFM determines the location and plots it on the floor plan. We have implemented a prototype of IP-PFM to demonstrate the end-to-end functionality of the indoor localization recognition chat bot system 1 .

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Section: Molnar and Kovacsmentioning

confidence: 99%

An Automatic Indoor Positioning Robot System Using Panorama Feature Matching

Chang¹,

Chen²,

Kambhampaty³

et al. 2021

International Conferences on Distributed Multimedia Systems

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“…18, the system achieves higher accuracy with the reference model than the ones built from Samples 1 or 5. 15 Recall that more test data become matching inputs with a denser point cloud, as shown in Fig. 15.…”

Section: Robustnessmentioning

confidence: 99%

“…Image-based localization systems allow users to locate themselves by simply taking photos from where they are. State-of-the-art systems such as Travi-Navi [38] employs image histogram matching, Liu et.al [20] utilized deeplearning approach for matching and tracking, while Huang et al [15] proposed image feature matching for panoramic images to obtain user's position. However, this process of image feature matching is slow due to heavy computation.…”

Section: Related Workmentioning

confidence: 99%

ViNav: A Vision-Based Indoor Navigation System for Smartphones

Jiang

Noreikis

Xiao

et al. 2019

IEEE Trans. on Mobile Comput.

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Smartphone-based indoor navigation services are desperately needed in indoor environments. However, the adoption of them has been relatively slow, due to the lack of fine-grained and up-to-date indoor maps, or the potentially high deployment and maintenance cost of infrastructure-based indoor localization solutions. This work proposes ViNav, a scalable and cost-efficient system that implements indoor mapping, localization, and navigation based on visual and inertial sensor data collected from smartphones. ViNav applies structure-from-motion (SfM) techniques to reconstruct 3D models of indoor environments from crowdsourced images, locates points of interest (POI) in 3D models, and compiles navigation meshes for path finding. ViNav implements image-based localization that identifies users' positions and facing directions, and leverages this feature to calibrate dead-reckoning-based user trajectories and sensor fingerprints collected along the trajectories. The calibrated information is utilized for building more informative and accurate indoor maps, and lowering the response delay of localization requests. According to our experimental results in a university building and a supermarket, the system works properly and our indoor localization achieves competitive performance compared with traditional approaches: in a supermarket, ViNav locates users within 2 seconds, with a distance error less than 1 meter and a facing direction error less than 6 degrees.

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“…There are several image-based localization techniques. Huang et al [15] used panorama images and visual features matching to calculate positions of a user. Gerstweiler et al [16] and Ventura et al [17] utilized Visual Simultaneous Localization and Mapping (VSLAM) to estimate user's indoor position.…”

Section: A Image-based Indoor Localizationmentioning

confidence: 99%

Low-Cost Mapping of RFID Tags Using Reader-Equipped Smartphones

Sato¹,

Noreikis

Xiao

2018

2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)

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This paper proposes a low-cost solution for mapping and locating UHF-band RFID tags in a 3D space using reader-equipped smartphones. Our solution includes a mobile augmented reality application for data collection and information visualization, and a cloud-based application server for calculating locations of the reader-equipped smartphones and the read RFID tags. Our solution applies computer vision and motion sensing techniques to track 3D locations of the RFID reader based on the visual and inertial sensor data collected from the companion smartphones. Meanwhile, it obtains the exact locations of RFID tags by calculating their relative positions from the readers based on the Angle of Arrival (AoA) concept. Our solution can be implemented with any low-cost fixed transmit power RFID readers, since it only requires the readers to report identifiers of read RFID tags. Furthermore, our solution does not require machine-controlled uniform movement of RFID readers, as it can handle the bias in the readings collected from randomly scattered positions. We have evaluated our solution with experiments in real environments using a commercially-off-the-shelf RFID reader and an Android phone. Results show that the average error in the positions of RFID tags is around 25cm for each of orthogonal axes on the floor plane, with the orders of RFID tags correctly detected in most cases.

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A fast image matching technique for the panoramic-based localization

Cited by 13 publications

References 15 publications

An Automatic Indoor Positioning Robot System Using Panorama Feature Matching

An Automatic Indoor Positioning Robot System Using Panorama Feature Matching

ViNav: A Vision-Based Indoor Navigation System for Smartphones

Low-Cost Mapping of RFID Tags Using Reader-Equipped Smartphones

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