A study on indoor pedestrian localization algorithms with foot-mounted sensors

Romanovas, Michailas; Goridko, Vadim; Al-Jawad, Ahmed; Schwaab, Manuel; Traechtler, Martin; Klingbeil, Lasse; Manoli, Yiannos

doi:10.1109/ipin.2012.6418886

Cited by 35 publications

(23 citation statements)

References 20 publications

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“…For better accuracy in the estimation of the step length or even the heading direction, it is preferable to use foot-mounted sensors [37], [38].…”

Section: ) Pedestrian Dead Reckoning and Its Limitationsmentioning

confidence: 99%

“…Note that foot-mounted IMUs [3], [38] with the Zero Velocity Update Method typically achieve a far better pedestrian dead reckoning accuracy, because of both better hardware and reduced noise due, as the IMU is attached to the foot that hits the ground as opposed to being stashed loose in a pocket, but they lack the convenience of consumer-grade smartphones casually worn in the pocket.…”

Section: )mentioning

confidence: 99%

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SignalSLAM: Simultaneous localization and mapping with mixed WiFi, Bluetooth, LTE and magnetic signals

Mirowski¹,

Ho²,

Yi³

et al. 2013

International Conference on Indoor Positioning and Indoor Navigation

146

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Abstract-Indoor localization typically relies on measuring a collection of RF signals, such as Received Signal Strength (RSS) from WiFi, in conjunction with spatial maps of signal fingerprints. A new technology for localization could arise with the use of 4G LTE telephony small cells, with limited range but with rich signal strength information, namely Reference Signal Received Power (RSRP). In this paper, we propose to combine an ensemble of available sources of RF signals to build multi-modal signal maps that can be used for localization or for network deployment optimization. We primarily rely on Simultaneous Localization and Mapping (SLAM), which provides a solution to the challenge of building a map of observations without knowing the location of the observer. SLAM has recently been extended to incorporate signal strength from WiFi in the so-called WiFi-SLAM. In parallel to WiFi-SLAM, other localization algorithms have been developed that exploit the inertial motion sensors and a known map of either WiFi RSS or of magnetic field magnitude. In our study, we use all the measurements that can be acquired by an off-the-shelf smartphone and crowd-source the data collection from several experimenters walking freely through a building, collecting time-stamped WiFi and Bluetooth RSS, 4G LTE RSRP, magnetic field magnitude, GPS reference points when outdoors, Near-Field Communication (NFC) readings at specific landmarks and pedestrian dead reckoning based on inertial data. We resolve the location of all the users using a modified version of Graph-SLAM optimization of the users poses with a collection of absolute location and pairwise constraints that incorporates multi-modal signal similarity. We demonstrate that we can recover the user positions and thus simultaneously generate dense signal maps for each WiFi access point and 4G LTE small cell, "from the pocket". Finally, we demonstrate the localization performance using selected single modalities, such as only WiFi and the WiFi signal maps that we generated.

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“…For better accuracy in the estimation of the step length or even the heading direction, it is preferable to use foot-mounted sensors [37], [38].…”

Section: ) Pedestrian Dead Reckoning and Its Limitationsmentioning

confidence: 99%

Section: )mentioning

confidence: 99%

SignalSLAM: Simultaneous localization and mapping with mixed WiFi, Bluetooth, LTE and magnetic signals

Mirowski¹,

Ho²,

Yi³

et al. 2013

International Conference on Indoor Positioning and Indoor Navigation

146

View full text Add to dashboard Cite

show abstract

“…The first one follows classical Inertial Navigation System mechanization where the measurements are integrated over time to produce the attitude angles of the mobile unit and its successive changes in positions [3,4]. Largely affected by the errors inherent to the low cost nature of the sensors, this strategy requires frequent sensor calibrations, which are possible thanks to external measurement sources (WiFi, UWB, RFID, etc.)…”

Section: Systemsmentioning

confidence: 99%

“…solely based on inertial sensors), these calibrations are mainly possible during the stance phase of the gait cycle during which the foot is tlat on the ground. Thanks to units rigidly attached to the foot, it is possible to sense these static phases and mitigate the sensors errors assuming zero velocity and/or zero angular rate states [5,6]. When the unit is held in hand, the problem becomes more complex since zero motion states can hardly be observed.…”

Section: Systemsmentioning

confidence: 99%

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Adaptative pedestrian displacement estimation with a smartphone

Renaudin¹,

Demeule²,

Ortiz³

2013

International Conference on Indoor Positioning and Indoor Navigation

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Pedestrian dead reckoning is one of the most promising processing strategies of inertial signals collected with a smartphone for autonomous indoor personal navigation. When the sensors are held in hand, step length models are usually used to estimate the walking distance. They combine stride frequency with a finite number of physiological and descriptive parameters that are calibrated with training data for each person. But even under steady conditions, several physiological conditions are impacting the walking gait and consequently these parameters. Frequent calibration is needed to tune these models prior to relying on free inertial navigation solutions in indoor locations. Two hybridization filters are proposed for calibrating the step length model and estimating the navigation solution. They integrate either GNSS standalone positions or GNSS Doppler depending on the coupling level. A data collection performed with four test subjects show the variations of these parameters for the same person during his journey and effectiveness of the calibration for improving the estimation of walking distances.Thanks to the new filters, the error on the travelled distance gets reduced to 7% with the loosely coupled filter and 2% with the tightly coupled filter.

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Overview of Indoor Navigation Techniques

Pasricha

2020

Position, Navigation, and Timing Technologies in the 21st Century

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A study on indoor pedestrian localization algorithms with foot-mounted sensors

Cited by 35 publications

References 20 publications

SignalSLAM: Simultaneous localization and mapping with mixed WiFi, Bluetooth, LTE and magnetic signals

SignalSLAM: Simultaneous localization and mapping with mixed WiFi, Bluetooth, LTE and magnetic signals

Adaptative pedestrian displacement estimation with a smartphone

Overview of Indoor Navigation Techniques

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