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Improving pedestrian navigation on iPhone in urban environments using deduced reckoning and turn detection
KTH, School of Computer Science and Communication (CSC).
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Pedestrian navigation on an iPhone today does not provide the accuracy

to place the pedestrian on the correct side of a street. A deciding issue

that prevents sufficient accuracy is the errors that occur when using

satellite positioning in urban environments. Another big problem is

that heading data has shown a tendency to be inaccurate.

Chapter 2 explains satellites navigation, navigation techniques, and

sensors. Chapter 4 describes how a prototype was developed. The prototype

uses deduced reckoning and turn detection to navigate a pedestrian

road network, without relying on satellite signals. The prototype is intended

to run on iPhone 5 and utilizes accelerometer, magnetometer

(compass), and gyroscope data together with detailed data about the

pedestrian network to accurately track a pedestrian. It features a turn

detection method that makes it possible to perform a logical traversal

of the road network, together with step detection and step length estimation

to move around.

The turn detection method was very effective and gave good results

when combined with logical traversal. For the two routes that were

tested the total error in distance estimation was about 3~7 % and for

both routes a close fit to the actual routes was achieved. For individual

parts of the routes the largest distance estimation errors varied between

3 and 15 %.

Place, publisher, year, edition, pages
2013. , 47 p.
National Category
Media and Communication Technology
URN: urn:nbn:se:kth:diva-170551OAI: diva2:838906
Available from: 2015-07-01 Created: 2015-07-01 Last updated: 2015-07-01Bibliographically approved

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