Pedestrian Dead Reckoning with Particle Filter for handheld smartphone

Racko, Jan; Brída, Peter; Perttula, Arto; Parviainen, Jussi; Collin, Jussi

doi:10.1109/ipin.2016.7743608

Cited by 46 publications

(45 citation statements)

References 20 publications

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“…On the other end of the spectrum, Pedestrian Dead Reckoning (PDR) is a popular technique which senses user motion to perform navigation. In PDR systems, either IMU sensors are handheld [22], [23] or mounted on different body parts, e.g. foot mounted [24], or torso-waist mounted [25].…”

Section: Related Workmentioning

confidence: 99%

A Radio-Inertial Localization and Tracking System with BLE Beacons Prior Maps

Jadidi¹,

Patel

Miró

et al. 2018

2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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In this paper, we develop a system for the lowcost indoor localization and tracking problem using radio signal strength indicator, Inertial Measurement Unit (IMU), and magnetometer sensors. We develop a novel and simplified probabilistic IMU motion model as the proposal distribution of the sequential Monte-Carlo technique to track the robot trajectory. Our algorithm can globally localize and track a robot with a priori unknown location, given an informative prior map of the Bluetooth Low Energy (BLE) beacons. Also, we formulate the problem as an optimization problem that serves as the Backend of the algorithm mentioned above (Front-end). Thus, by simultaneously solving for the robot trajectory and the map of BLE beacons, we recover a continuous and smooth trajectory of the robot, corrected locations of the BLE beacons, and the timevarying IMU bias. The evaluations achieved using hardware show that through the proposed closed-loop system the localization performance can be improved; furthermore, the system becomes robust to the error in the map of beacons by feeding back the optimized map to the Front-end.

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Section: Related Workmentioning

confidence: 99%

A Radio-Inertial Localization and Tracking System with BLE Beacons Prior Maps

Jadidi¹,

Patel

Miró

et al. 2018

2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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“…In the field of indoor localisation, they are most popular among the fusion of inertial sensors, especially when applied to PDR [3,66,126]. Hsu et al [66] considered the fusion of a foot-mounted IMU and GPS signals to rectify noise drift.…”

Section: Methods Of Fusionmentioning

confidence: 99%

“…There, the PF was scrutinised against energy efficiency, in addition to positioning accuracy. Racko et al [126] also used particle filtering in service of PDR. They did this by predicting steps and heading from an IMU.…”

Section: Methods Of Fusionmentioning

confidence: 99%

Sensor Modalities and Fusion for Robust Indoor Localisation

Kozłowski¹,

Santos-Rodríguez²,

Piechocki³

2019

ICST Transactions on Ambient Systems

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The importance of accurate and efficient positioning and tracking is widely understood. However, there is a pressing lack of progress in the standardisation of methods, as well as generalised framework of their evaluation. The aim of this survey is to discuss the currently prevalent and emerging types of sensors used for location estimation. The intent of this review is to take account of this taxonomy and to provide a wider understanding of the current state-of-the-art. To that end, we outline various sensor modalities, as well as popular fusion and integration techniques, discussing how their combinations can help in various application settings. Firstly, we present the fundamental mechanics behind sensors employed by the localisation community. Furthermore we outline the formal theory behind prominent fusion methods and provide exhaustive implementation examples of each. Finally, we provide points for future discussion regarding localisation sensing, fusion and integration methods.to user's preference and experience. This makes the relative selection space large, and frequently open to interpretation with regard to available resources and constraints.Whilst the survey literature pertaining to localisation systems and methods is large [54,171,181], there exists very little in the way of localisation-centric sensor utilisation. This encompasses the use of bespoke [39] or off-the-shelf [13,118] sensors, specifically for the use of location estimation, robustification and optimisation. This area is extensive [20,35,57,83,136,146], yet very often bundled along with localisation technology surveys, without subsequent scrutiny. We aim to close this gap, by reviewing sensors, their fusion and utilisation as applied to localisation, in contrast to localisation methods, technologies and implementations themselves.Most of the existing localisation surveys include technology-specific reviews [30,54,92,181]. They concentrate upon the methods and algorithms related to indoor localisation [30,92], techniques and technologies [181]. Some work also addresses localisation from the perspective of the device itself, such as smartphones [171]. Xiao et al. study [171] is the most closely related work to our proposed examination. The main difference is, that instead of reviewing the devices as

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“…Because of the accumulative error, the localization accuracy of mounted-PDR will decrease with time, thus a Zero Velocity Update (ZUPT) algorithm is used to control the accumulative error [11,12,13]. Since pedestrians’ burden is increased, mounted-PDR is less convenient for ordinary consumers in many scenarios [14]. To get rid of the limitation that the device must be mounted to the body, more flexible handheld-PDR systems have been universally used.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Dead Reckoning Based on Motion Mode Recognition Using a Smartphone

Wang

Liu

et al. 2018

Sensors

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This paper presents a pedestrian dead reckoning (PDR) approach based on motion mode recognition using a smartphone. The motion mode consists of pedestrian movement state and phone pose. With the support vector machine (SVM) and the decision tree (DT), the arbitrary combinations of movement state and phone pose can be recognized successfully. In the traditional principal component analysis based (PCA-based) method, the obtained horizontal accelerations in one stride time interval cannot be guaranteed to be horizontal and the pedestrian’s direction vector will be influenced. To solve this problem, we propose a PCA-based method with global accelerations (PCA-GA) to infer pedestrian’s headings. Besides, based on the further analysis of phone poses, an ambiguity elimination method is also developed to calibrate the obtained headings. The results indicate that the recognition accuracy of the combinations of movement states and phone poses can be 92.4%. The 50% and 75% absolute estimation errors of pedestrian’s headings are 5.6° and 9.2°, respectively. This novel PCA-GA based method can achieve higher accuracy than traditional PCA-based method and heading offset method. The localization error can reduce to around 3.5 m in a trajectory of 164 m for different movement states and phone poses.

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Pedestrian Dead Reckoning with Particle Filter for handheld smartphone

Cited by 46 publications

References 20 publications

A Radio-Inertial Localization and Tracking System with BLE Beacons Prior Maps

A Radio-Inertial Localization and Tracking System with BLE Beacons Prior Maps

Sensor Modalities and Fusion for Robust Indoor Localisation

Pedestrian Dead Reckoning Based on Motion Mode Recognition Using a Smartphone

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