Abstract. The objective of this research is to improve traffic safety through collecting and distributing up-to-date road surface condition information using mobile phones. Road surface condition information is seen useful for both travellers and for the road network maintenance. The problem we consider is to detect road surface anomalies that, when left unreported, can cause wear of vehicles, lesser driving comfort and vehicle controllability, or an accident. In this work we developed a pattern recognition system for detecting road condition from accelerometer and GPS readings. We present experimental results from real urban driving data that demonstrate the usefulness of the system. Our contributions are: 1) Performing a throughout spectral analysis of tri-axis acceleration signals in order to get reliable road surface anomaly labels. 2) Comprehensive preprocessing of GPS and acceleration signals. 3) Proposing a speed dependence removal approach for feature extraction and demonstrating its positive effect in multiple feature sets for the road surface anomaly detection task. 4) A framework for visually analyzing the classifier predictions over the validation data and labels.

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As satellite signals, e.g. GPS, are severely degraded indoors or not available at all, other methods are needed for indoor positioning. In this paper, we propose methods for combining information from inertial sensors, indoor map, and WLAN signals for pedestrian indoor navigation. We present results of field tests where complementary extended Kalman filter was used to fuse together WLAN signal strengths and signals of an inertial sensor unit including one gyro and three-axis accelerometer. A particle filter was used to combine the inertial data with map information. The results show that both the map information and WLAN signals can be used to improve the pedestrian dead reckoning estimate based on inertial sensors.

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As satellite signals, e.g. GPS, are severely degraded indoors or not available at all, other methods are needed for indoor positioning. In this paper, we propose methods for combining information from inertial sensors, indoor map, and WLAN signals for pedestrian indoor navigation. We present results of field tests where complementary extended Kalman filter was used to fuse together WLAN signal strengths and signals of an inertial sensor unit including one gyro and three-axis accelerometer. A particle filter was used to combine the inertial data with map information. The results show that both the map information and WLAN signals can be used to improve the pedestrian dead reckoning estimate based on inertial sensors.

In this paper, a method and system for gyrocompassing based on a low-cost micro-electro-mechanical (MEMS) gyroscope are described. The proposed setup is based on the choice of a gyroscope with specified bias instability better than 2 deg h −1 and on careful error compensation. The gyroscope is aligned parallel to the local level, which helps to eliminate the g-sensitivity effect but also sacrifices a fraction of the Earth's rotation rate that can be observed. The additive bias is compensated for by rotating the sensor mechanically and by extended Kalman filtering. In this paper, it is demonstrated that the proposed system is capable of observing the Earth's rotation, and the north finding results show that a two-sigma accuracy of 4.03 • was attained at latitude 61 • N. With current MEMS gyroscopes, the system requires hours of time to achieve this accuracy, but the results demonstrate the theoretical accuracy potential of these small self-contained, low-cost sensors.

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