A reliable and accurate indoor localization method using phone inertial sensors

Li, Fan; Zhao, Chunlei; Ding, Guanzhong; Gong, Jian; Liu, Chenxing; Zhao, Feng

doi:10.1145/2370216.2370280

Cited by 497 publications

(334 citation statements)

References 20 publications

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“…First, the initial heading offset between user heading and device forward heading is zero, since users need to hold device in hand and gaze at it when they start the localization application. Second, the initial user heading and related device attitude are assumed to be known a priori [21,22], which can be obtained by Global Position System (GPS) tracking when the user enters a building, landmarks, or WiFi localization.…”

Section: User Heading Estimationmentioning

confidence: 99%

Robust Heading Estimation for Indoor Pedestrian Navigation Using Unconstrained Smartphones

Deng

Liu

et al. 2018

Wireless Communications and Mobile Computing

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Heading estimation using inertial sensors built-in smartphones has been considered as a central problem for indoor pedestrian navigation. For practical daily lives, it is necessary for heading estimation to allow an unconstrained use of smartphones, which means the varying device carrying positions and orientations. As a result, three special human body motion states, namely, random hand movements, carrying position transitions, and user turns, are introduced. However, most existing heading estimation approaches neglect the three motion states, which may render large estimation errors. We propose a robust heading estimation system adapting to the unconstrained use of smartphones. A novel detection and classification method is developed to detect the three motion states timely and discriminate them accurately. For normal working, the user heading is estimated by a PCA-based approach. If a user turn occurs, it is estimated by adding horizontal heading change to previous user heading directly. If one of the other two motion states occurs, it is obtained by averaging estimation results of the adjacent normal walking steps. Finally, an outlier filtering algorithm is developed to smooth the estimation results. Experimental results show that our approach is capable of handling the unconstrained situation of smartphones and outperforms previous approaches in terms of accuracy and applicability.

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Section: User Heading Estimationmentioning

confidence: 99%

Robust Heading Estimation for Indoor Pedestrian Navigation Using Unconstrained Smartphones

Deng

Liu

et al. 2018

Wireless Communications and Mobile Computing

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“…(4) Smart phone, equipped with accelerometer and magnetometer and running a RollCaller client connecting with the back-end server. It is carried by the user to measure the displacement of himself using IMU-based displacement measurement [12], [13], [24]. Also, it acts as an interface to acquire the information of the item the user wants, and remind the user to stop to take that item.…”

Section: A System Overviewmentioning

confidence: 99%

RollCaller: User-friendly indoor navigation system using human-item spatial relation

Guo

Yang

et al. 2014

IEEE INFOCOM 2014 - IEEE Conference on Computer Communications

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Abstract-Indoor navigation has received much attention in academics and industry in recent years. Previous methods often attempt to locate users with various localization algorithms in combination with an indoor map, so they need expensive infrastructures deployed in advance. In this study, we propose to utilize existing indoor objects attached RFID tags and the reader to navigate the user to the destination, without need of any extra hardware. The key insight is that the personal movement takes an impact on the Doppler frequency shift values collected from the indoor objects when getting close to the tag. Such local humanitem spatial relation is leveraged to infer the users position and further navigate user to destination step by step. We implement a prototype navigation system, called RollCaller, and conduct comprehensive experiments to examine its performance.

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“…A comprehensive study is presented on design and hardware selection of robots [14,15]. Different researchers have come up with different localization technologies for indoor environment; WiFi [16], ultrasonic [17,18], RFID tags [19][20][21], FM [22], magnetic field [23,24], IMU [25], cameras, lasers and sonar [26,27]. Most of these technologies and solutions have some disadvantages and limitations especially when they are used in the indoor environment.…”

Section: Introductionmentioning

confidence: 99%

A Robust Localization, Slip Estimation, and Compensation System for WMR in the Indoor Environments

Ullah

Zhang

et al. 2018

Symmetry

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Abstract:A novel approach is proposed for the path tracking of a Wheeled Mobile Robot (WMR) in the presence of an unknown lateral slip. Much of the existing work has assumed pure rolling conditions between the wheel and ground. Under the pure rolling conditions, the wheels of a WMR are supposed to roll without slipping. Complex wheel-ground interactions, acceleration and steering system noise are the factors which cause WMR wheel slip. A basic research problem in this context is localization and slip estimation of WMR from a stream of noisy sensors data when the robot is moving on a slippery surface, or moving at a high speed. DecaWave based ranging system and Particle Filter (PF) are good candidates to estimate the location of WMR indoors and outdoors. Unfortunately, wheel-slip of WMR limits the ultimate performance that can be achieved by real-world implementation of the PF, because location estimation systems typically partially rely on the robot heading. A small error in the WMR heading leads to a large error in location estimation of the PF because of its cumulative nature. In order to enhance the tracking and localization performance of the PF in the environments where the main reason for an error in the PF location estimation is angular noise, two methods were used for heading estimation of the WMR (1): Reinforcement Learning (RL) and (2): Location-based Heading Estimation (LHE). Trilateration is applied to DecaWave based ranging system for calculating the probable location of WMR, this noisy location along with PF current mean is used to estimate the WMR heading by using the above two methods. Beside the WMR location calculation, DecaWave based ranging system is also used to update the PF weights. The localization and tracking performance of the PF is significantly improved through incorporating heading error in localization by applying RL and LHE. Desired trajectory information is then used to develop an algorithm for extracting the lateral slip along X-and Y-axis from the PF estimated position of the WMR, the lateral slip along X-and Y-axis is then used to take some corrective measures. Lateral slip information is also used to find the direction along which WMR has to move to get back along the desired trajectory. Simulation results show that our proposed LHE and RL heading estimation methods significantly improve the PF localization and tracking performance on a slippery surface in both indoor and outdoor environments. The simulation results also show that the accurate locations of WMR and desired path information are used to estimate and compensate the lateral slip.

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A reliable and accurate indoor localization method using phone inertial sensors

Cited by 497 publications

References 20 publications

Robust Heading Estimation for Indoor Pedestrian Navigation Using Unconstrained Smartphones

Robust Heading Estimation for Indoor Pedestrian Navigation Using Unconstrained Smartphones

RollCaller: User-friendly indoor navigation system using human-item spatial relation

A Robust Localization, Slip Estimation, and Compensation System for WMR in the Indoor Environments

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