Pedestrian localisation for indoor environments

By Oliver J. Woodman, Robert K. Harle. Ubiquitous Computing, 2008

569 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/437739f2b3e2bffbbc3b59a09c2b25e952fbf443

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In Computer Vision and Pattern Recognition

In Electrical and Electronic Engineering

In Signal Processing

Citations in the corpus

Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications (2017, methodology): “, [70] and [71]) were based on the utilization of a mapbased or fingerprinting RSS localization method.”
A Survey of Selected Indoor Positioning Methods for Smartphones (2017, methodology): “In this particular application, a particle filter provides a natural and intuitive way of incorporating building plan information into position estimation, by applying the direct constraint to each particle as was implemented in [49]–[51], [55].”
WILL: Wireless indoor localization without site survey (2012, background): “However, due to the presence of noise in the sensor readings, error accumulates rapidly and can reach up to 100 meters after one minute of operation [23].”
Zee: zero-effort crowdsourcing for indoor localization (2012, background): “Some of the above challenges have been addressed in [29, 30].”
Indoor localization in multi-floor environments with reduced effort (2010, background): “There is also some other work on studying how to track a user in a multi-floor building such as [14], but they typically require some other specific sensors instead of using only the wireless adapters.”
LifeLogging: Personal Big Data (2014, background): “For example, the capture frequency from sensors can range from +30Hz for devices such as accelerometers, Zhang et al. (2012), to less than one reading per day for course-grained location changes or personal encounters, Byrne et al.”
Locating in fingerprint space: wireless indoor localization with little human intervention (2012, background): “However due to the presence of noise in accelerometer readings, error accumulates rapidly and can reach up to 100 meters after one minute of operation [33].”
A reliable and accurate indoor localization method using phone inertial sensors (2012, methodology): “Woodman and Harle [25] proposed a dead reckoning approach, using a foot-mounted IMU device for data collection.”
Magicol: Indoor Localization Using Pervasive Magnetic Field and Opportunistic WiFi Sensing (2015, unknown): “, dead reckoning) system [10]–[12].”
No need to war-drive: unsupervised indoor localization (2012, background): “The key problem is that dead-reckoning suffers from the accumulation of error, and can grow cubically even with foot mounted accelerometers [24].”
Walk detection and step counting on unconstrained smartphones (2013, methodology): “In the PDR field, walk detection is essential to avoid applying expensive analysis algorithms during periods of nonmotion, and step counting is necessary for many algorithms that assume a step length and estimate a movement [6, 16, 58].”
Smartphones Based Crowdsourcing for Indoor Localization (2015, background): “However due to the presence of noise in accelerometer readings, error accumulates rapidly and can reach up to 100 meters after one minute of operation [37].”
A Survey of Indoor Inertial Positioning Systems for Pedestrians (2013, methodology): “Such SHSINS systems naturally suffer from the same drift accrual found in pure INS systems and they are used merely because SHS outputs are simpler to handle when working with the higherlevel filters necessary to incorporate other constraints such as maps [53].”
Indoor localization without infrastructure using the acoustic background spectrum (2011, unknown): “Woodman and Harle [31] used a foot-mounted accelerometer to locate a person within a previously-mapped building.”