Indoor Navigation with a Smartphone Fusing Inertial and WiFi Data via Factor Graph Optimization

Nowicki, Michał; Skrzypczyński, Piotr

doi:10.1007/978-3-319-29003-4_16

Cited by 22 publications

(27 citation statements)

References 18 publications

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“…Our future research will focus on integrating Fast-ABLE in the graph-based multi-sensor personal localization scheme for mobile devices we have introduced in [29].…”

Section: Discussionmentioning

confidence: 99%

Real-Time Visual Place Recognition for Personal Localization on a Mobile Device

Nowicki

Wietrzykowski

Skrzypczyński

2017

Wireless Pers Commun

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The paper presents an approach to indoor personal localization on a mobile device based on visual place recognition. We implemented on a smartphone two state-ofthe-art algorithms that are representative to two different approaches to visual place recognition: FAB-MAP that recognizes places using individual images and ABLE-M that utilizes sequences of images. These algorithms are evaluated in environments of different structure, focusing on problems commonly encountered when a mobile device camera is used. The conclusions drawn from this evaluation are guidelines to design the FastABLE system, which is based on the ABLE-M algorithm but introduces major modifications to the concept of image matching. The improvements radically cut down the processing time and improve scalability, making it possible to localize the user in long image sequences with the limited computing power of a mobile device. The resulting place recognition system compares favorably to both the ABLE-M and the FAB-MAP solutions in the context of real-time personal localization.

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“…Our future research will focus on integrating Fast-ABLE in the graph-based multi-sensor personal localization scheme for mobile devices we have introduced in [29].…”

Section: Discussionmentioning

confidence: 99%

Real-Time Visual Place Recognition for Personal Localization on a Mobile Device

Nowicki

Wietrzykowski

Skrzypczyński

2017

Wireless Pers Commun

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“…Future work will focus on locating the user or robot using WiFi data clusters much smaller than whole floors. Knowledge of the corridor in which a device is located often can be sufficient for indoor navigation when combined with other premises about the user position [17]. We also plan to implement a more detailed location model than the one available in OpenFABMAP.…”

Section: Discussionmentioning

confidence: 99%

Adopting the FAB-MAP Algorithm for Indoor Localization with WiFi Fingerprints

Wietrzykowski¹,

Nowicki²,

Skrzypczyński³

2017

Advances in Intelligent Systems and Computing

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Abstract. Personal indoor localization is usually accomplished by fusing information from various sensors. A common choice is to use the WiFi adapter that provides information about Access Points that can be found in the vicinity. Unfortunately, state-of-the-art approaches to WiFi-based localization often employ very dense maps of the WiFi signal distribution and require a time-consuming process of parameter selection. On the other hand, camera images are commonly used for visual place recognition, detecting whenever the user observes a scene similar to the one already recorded in a database. Visual place recognition algorithms can work with sparse databases of recorded scenes and are in general simple to parametrize. Therefore, we propose a WiFi-based global localization method employing the structure of the well-known FAB-MAP visual place recognition algorithm. Similarly to FAB-MAP, our method uses Chow-Liu trees to estimate a joint probability distribution of re-observation of a place given a set of features extracted at places visited so far. However, we are the first who apply this idea to recorded WiFi scans instead of visual words. The new method is evaluated on the UJIIndoorLoc dataset used in the EvAAL competition, allowing a fair comparison with other solutions.

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“…For the PDR based cost function, we define the weight matrix as follows according to the contribution in [41]:…”

Section: Ccf Forming and Graph Optimizationmentioning

confidence: 99%

An Improved Pedestrian Ttracking Method Based on Wi-Fi Fingerprinting and Pedestrian Dead Reckoning

Bao

Tang

Guo

et al. 2020

Sensors

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Wi-Fi based positioning has great potential for use in indoor environments because Wi-Fi signals are near-ubiquitous in many indoor environments. With a Reference Fingerprint Map (RFM), fingerprint matching can be adopted for positioning. Much assisting information can be adopted for increasing the accuracy of Wi-Fi based positioning. One of the most adopted pieces of assisting information is the Pedestrian Dead Reckoning (PDR) information derived from inertial measurements. This is widely adopted because the inertial measurements can be acquired through a Commercial Off The Shelf (COTS) smartphone. To integrate the information of Wi-Fi fingerprinting and PDR information, many methods have adopted filters, such as Kalman filters and particle filters. A new methodology for integration of Wi-Fi fingerprinting and PDR is proposed using graph optimization in this paper. For the Wi-Fi based fingerprinting part, our method adopts the state-of-art hierarchical structure and the Penalized Logarithmic Gaussian Distance (PLGD) metric. In the integration part, a simple extended Kalman filter (EKF) is first used for integration of Wi-Fi fingerprinting and PDR results. Then, the tracking results are adopted as initial values for the optimization block, where Wi-Fi fingerprinting and PDR results are adopted to form an concentrated cost function (CCF). The CCF can be minimized with the aim of finding the optimal poses of the user with better tracking results. With both real-scenario experiments and simulations, we show that the proposed method performs better than classical Kalman filter based and particle filter based methods with both less average and maximum positioning error. Additionally, the proposed method is more robust to outliers in both Wi-Fi based and PDR based results, which is commonly seen in practical situations.

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Indoor Navigation with a Smartphone Fusing Inertial and WiFi Data via Factor Graph Optimization

Cited by 22 publications

References 18 publications

Real-Time Visual Place Recognition for Personal Localization on a Mobile Device

Real-Time Visual Place Recognition for Personal Localization on a Mobile Device

Adopting the FAB-MAP Algorithm for Indoor Localization with WiFi Fingerprints

An Improved Pedestrian Ttracking Method Based on Wi-Fi Fingerprinting and Pedestrian Dead Reckoning

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