Improving pedestrian navigation on iPhone in urban environments using deduced reckoning and turn detection
Abstract: 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 %.
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