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Journal ArticleDOI

Pedestrian tracking with shoe-mounted inertial sensors

Eric Foxlin
- 01 Nov 2005 - 
- Vol. 25, Iss: 6, pp 38-46
TLDR
The NavShoe device provides not only robust approximate position, but also an extremely accurate orientation tracker on the foot, which can greatly reduce the database search space for computer vision, making it much simpler and more robust.
Abstract
A navigation system that tracks the location of a person on foot is useful for finding and rescuing firefighters or other emergency first responders, or for location-aware computing, personal navigation assistance, mobile 3D audio, and mixed or augmented reality applications. One of the main obstacles to the real-world deployment of location-sensitive wearable computing, including mixed reality (MR), is that current position-tracking technologies require an instrumented, marked, or premapped environment. At InterSense, we've developed a system called NavShoe, which uses a new approach to position tracking based on inertial sensing. Our wireless inertial sensor is small enough to easily tuck into the shoelaces, and sufficiently low power to run all day on a small battery. Although it can't be used alone for precise registration of close-range objects, in outdoor applications augmenting distant objects, a user would barely notice the NavShoe's meter-level error combined with any error in the head's assumed location relative to the foot. NavShoe can greatly reduce the database search space for computer vision, making it much simpler and more robust. The NavShoe device provides not only robust approximate position, but also an extremely accurate orientation tracker on the foot.

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Citations
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Proceedings ArticleDOI

Estimation of IMU and MARG orientation using a gradient descent algorithm

TL;DR: This paper presents a novel orientation algorithm designed to support a computationally efficient, wearable inertial human motion tracking system for rehabilitation applications, applicable to inertial measurement units (IMUs) consisting of tri-axis gyroscopes and accelerometers, and magnetic angular rate and gravity sensor arrays that also include tri- axis magnetometers.

An introduction to inertial navigation

TL;DR: This work introduces inertial navigation, focusing on strapdown systems based on MEMS devices, and concludes that whilst MEMS IMU technology is rapidly improving, it is not yet possible to build a MEMS based INS which gives sub-meter position accuracy for more than one minute of operation.
Journal ArticleDOI

A Survey of Indoor Inertial Positioning Systems for Pedestrians

TL;DR: It is concluded that PDR techniques alone can offer good short- to medium- term tracking under certain circumstances, but that regular absolute position fixes from partner systems will be needed to ensure long-term operation and to cope with unexpected behaviours.
Journal ArticleDOI

Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers

TL;DR: How human physical activity can be classified using on-body accelerometers, with a major emphasis devoted to the computational algorithms employed for this purpose, is discussed.
Journal ArticleDOI

High-precision, consistent EKF-based visual-inertial odometry

TL;DR: A novel, real-time EKF-based VIO algorithm is proposed, which achieves consistent estimation by ensuring the correct observability properties of its linearized system model, and performing online estimation of the camera-to-inertial measurement unit (IMU) calibration parameters.
References
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Proceedings ArticleDOI

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Proceedings ArticleDOI

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Proceedings ArticleDOI

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Proceedings ArticleDOI

Personal position measurement using dead reckoning

TL;DR: This paper compares position measurement techniques using dead reckoning, and has compared a number of sensors that can be used to achieve a robust and accurate dead reckoning system.

A Personal Dead Reckoning Module

Thomas Judd
TL;DR: Point Research Corporation of Santa Ana, CA has developed a new lightweight miniature Dead Reckoning Module (DRM) for drift-free navigation by personnel on foot as discussed by the authors, which is protected under U.S. patent number 5,583,776.
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