Jussi Collin

Distributed road surface condition monitoring using mobile phones

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.

Pedestrian Navigation Based on Inertial Sensors, Indoor Map, and WLAN Signals

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. The results with different combinations of the available sensor information are compared.

Bias Prediction for MEMS Gyroscopes

MEMS gyroscopes are gaining popularity because of their low manufacturing costs in large quantities. For navigation system engineering, this presents a challenge because of strong nonstationary noise processes, such as 1/f noise, in the output of MEMS gyros. In practice, on-the-fly calibration is often required before the gyroscope data are useful and comparable to more expensive optical gyroscopes. In this paper, we focus on an important part of MEMS gyro processing, i.e., predicting the future bias given calibration data with known (usually zero) input. We derive prediction algorithms based on Kalman filtering and the computation of moving averages, and compare their performance against simple averaging of the calibration data based on both simulations and real measured data. The results show that it is necessary to model fractional noise in order to consistently predict the bias of a modern MEMS gyro, but the complexity of the Kalman filter approach makes other methods, such as the moving averages, appealing.

Differential barometry in personal navigation

In many personal navigation applications accurate altitude information is required. Finding a correct floor in a tall building or precise guidance in multi-layer intersection is not possible with two-dimensional navigation system. Satellite navigation systems provide altitude information, but accuracy is not necessarily sufficient for this kind of situations. Barometers can be used to improve the accuracy and availability of the altitude solution. The objective of this study was to find relevant error sources of barometer based altitude in the context of personal navigation. MEMS barometer data were collected in several scenarios where different disturbances affect the pressure reading. To separate error sources, reference barometer at known altitude was used. The results show that barometers in differential mode provide highly accurate altitude solution, but local disturbances in pressure need to be taken into account in the application design.

Pedestrian navigation based on inertial sensors, indoor map, and WLAN signals

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.

Application of particle filters for indoor positioning using floor plans

This paper presents a numerical approach to the pedestrian map-matching problem using building plans. The proposed solution is based on a sequential Monte Carlo method, so called particle filtering. This algorithm can be adapted for implementation on real-time pedestrian navigation systems using low-cost MEMS gyroscopes and accelerometers as dead-reckoning sensors. The algorithm reliability and accuracy performance was investigated using simulated data typical for pedestrians walking inside building. The results show that this map-aided dead reckoning system is able to provide accurate indoor positioning for long periods of time without using GPS data.

Distributed Indoor Positioning System With Inertial Measurements and Map Matching

Accurate position information is nowadays very important in many applications. For instance, maintaining the situation awareness in command center in emergency operations is very crucial. Due to signal strength attenuation and multipath, Global Navigation Satellite Systems are not suitable for indoor navigation purposes. Radio network-based positioning techniques, such as wireless local area network, require local infrastructure that is often vulnerable in emergency situations. We propose here a distributed system for personal positioning based on inertial sensors. The system consists of an inertial measurement unit (IMU) connected to a radio carried by a person and the server connected to another radio. Step length and heading estimation is computed in the IMU and sent to the server. On the server side, the position is estimated using particle filter-based map matching. The benefit of the distributed architecture is that the computational capacity can be kept very low on the user side, which leads to long operation time as power consumption also remains very low.

A raim approach to GNSS outlier and cycle slip detection using L1 carrier phase time-differences

Cycle slips are a common error source in Global Navigation Satellite System (GNSS) carrier phase measurements. In this paper, the cycle slip problem is approached using Receiver Autonomous Integrity Monitoring (RAIM) methodology. Carrier phase measurements are used here in a single-receiver time-differential positioning method where integer ambiguities are canceled, but any cycle slips remain. The performance of the method was assessed by comparing the detection results to a Real-Time Kinematic (RTK) solution and by manual data examination. Postprocessing results obtained using authentic Global Positioning System (GPS) measurements logged by low-cost single-frequency receivers show that the method is able to reliably detect and identify single errors but fails in an exemplary multiple outlier scenario. As no reference receiver is needed, the method is a potential means to produce cycle-slip-corrected data usable in any postprocessing application.

Using a MEMS gyroscope to measure the Earth’s rotation for gyrocompassing applications

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.

MEMS IMU Carouseling for Ground Vehicles

Microelectromechanical system (MEMS) gyroscopes have advantageous properties for orientation sensing and navigation as they are small, low cost, and consume little power. However, the significant noise at low frequencies results in large orientation errors as a function of time. Controlled physical rotation of the gyroscope can be used to remove the constant part of the gyro errors and reduce low-frequency noise. As adding motors for this would increase the system cost, it would be advantageous to attach gyros to a rotating platform that is already built in the vehicle. In this paper, we present theory and results for novel navigation systems where an inertial measurement unit (IMU) is attached to the wheel of a ground vehicle. The results show that a low-cost MEMS IMU can provide a very accurate navigation solution using this placement option.