Dead reckoning supports stereo vision in pedestrians tracking

Cinotti, Tullio Salmon; Stefano, Luigi Di; Raffa, Giuseppe Maria; Roffia, Luca; Pettinari, Marina; Mola, M.

doi:10.1109/percomw.2006.35

Cited by 8 publications

(3 citation statements)

References 4 publications

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“…Most PDR systems use inertial sensors (such as an accelerometer, gyroscope, or digital compass) to measure step length and heading direction. Some also use other sensors, such as cameras [12], ultrasound [13], RFID [14,15], or laser [16], to calibrate their results. Generally, depending on where the sensors are placed, previous studies classified PDR systems into two types: foot- and waist-mounted.…”

Section: Introductionmentioning

confidence: 99%

On Calibrating the Sensor Errors of a PDR-Based Indoor Localization System

Lan

Shih

2013

Sensors

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Many studies utilize the signal strength of short-range radio systems (such as WiFi, ultrasound and infrared) to build a radio map for indoor localization, by deploying a large number of beacon nodes within a building. The drawback of such an infrastructure-based approach is that the deployment and calibration of the system are costly and labor-intensive. Some prior studies proposed the use of Pedestrian Dead Reckoning (PDR) for indoor localization, which does not require the deployment of beacon nodes. In a PDR system, a small number of sensors are put on the pedestrian. These sensors (such as a G-sensor and gyroscope) are used to estimate the distance and direction that a user travels. The effectiveness of a PDR system lies in its success in accurately estimating the user's moving distance and direction. In this work, we propose a novel waist-mounted based PDR that can measure the user's step lengths with a high accuracy. We utilize vertical acceleration of the body to calculate the user's change in height during walking. Based on the Pythagorean Theorem, we can then estimate each step length using this data. Furthermore, we design a map matching algorithm to calibrate the direction errors from the gyro using building floor plans. The results of our experiment show that we can achieve about 98.26% accuracy in estimating the user's walking distance, with an overall location error of about 0.48 m.

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Section: Introductionmentioning

confidence: 99%

On Calibrating the Sensor Errors of a PDR-Based Indoor Localization System

Lan

Shih

2013

Sensors

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“…Active Bat is an example of a hybrid system. Other examples of hybrid systems include [ [6], [7], [8], [22]]. The PlaceLab positioning system is not limited to listening for transmissions for 802.11 access points.…”

Section: Empiricalmentioning

confidence: 99%

WHLocator

Lemieux

Lutfiyya

2009

Proceedings of the 2009 International Conference on Pervasive Services

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“…Where context sources simply push context to the CB, e.g. GPS data and inertial tracking co-ordinates [5], and context providers allow the CB to request specific context, e.g. from multi sensor platforms like Sun Spots [6] or Motes [7].…”

Section: Context-aware Platformmentioning

confidence: 99%

Towards Context-Aware Telecom End User Services through SOA

Mohr

Raffa

Pettinari

et al. 2009

Service-Oriented Computing – ICSOC 2007

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In this paper a SOA inspired context-aware platform for enabling context-aware Telecom services is presented. The SOA related components are highlighted and the platform's integration into a Telecom provider's service architecture is described-pointing out challenges in terms of protocols and semantic interoperability. An example application bridging the gap between data collected through a user's mobile phone and a web based portal in order to provide a Virtual Location Application through our platform is presented.

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Dead reckoning supports stereo vision in pedestrians tracking

Cited by 8 publications

References 4 publications

On Calibrating the Sensor Errors of a PDR-Based Indoor Localization System

On Calibrating the Sensor Errors of a PDR-Based Indoor Localization System

WHLocator

Towards Context-Aware Telecom End User Services through SOA

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