Contributions to Positioning Methods on Low-Cost Devices

Global Navigation Satellite System (GNSS) receivers are common in modern consumer devices that make use of position information, e.g., smartphones and personal navigation assistants. With a GNSS receiver, a position solution with an accuracy in the order of five meters is usually available if the reception conditions are benign, but the performance degrades rapidly in less favorable environments and, on the other hand, a better accuracy would be beneficial in some applications. This thesis studies advanced methods for processing the measurements of low-cost devices that can be used for improving the positioning performance. The focus is on GNSS receivers and microelectromechanical (MEMS) inertial sensors which have become common in mobile devices such as smartphones. First, methods to compensate for the additive bias of a MEMS gyroscope are investigated. Both physical slewing of the sensor and mathematical modeling of the bias instability process are considered. The use of MEMS inertial sensors for pedestrian navigation indoors is studied in the context of map matching using a particle filter. A high-sensitivity GNSS receiver is used to produce coarse initialization information for the filter to decrease the computational burden without the need to exploit local building infrastructure. Finally, a cycle slip detection scheme for stand-alone single-frequency GNSS receivers is proposed. Experimental results show that even a MEMS gyroscope can reach an accuracy suitable for North seeking if the measurement errors are carefully modeled and eliminated. Furthermore, it is seen that even a relatively coarse initialization can be adequate for long-term indoor navigation without an excessive computational burden if a detailed map is available. The cycle slip detection results suggest that even small cycle slips can be detected with mass-market GNSS receivers, but the detection rate needs to be improved

Dissemination of GNSS RTK using MQTT

Precise positioning using Global Navigation Satellite System (GNSS) requires the GNSS receivers to compensate for the errors caused by distortion in the GNSS signal's path due to atmospheric conditions. The Real Time Kinematics (RTK) technique uses terrestrial reference stations that continuously monitor the quality of GNSS signals and provide information that can be be used by the GNSS receivers in the vicinity of a reference station to compensate for the errors. In this paper, we explore the performance of disseminating the RTK correction information using the Message Queuing Telemetry Transport (MQTT) protocol over 5G. We also compare the indirection costs (latency overheads) of using MQTT over 5G to Ethernet and Wi-Fi, our baselines for high-speed and wireless connectivity respectively, and we highlight the impact of 5G power savings when disseminating GNSS RTK using MQTT.Peer reviewe

Error Modelling for Multi-Sensor Measurements in Infrastructure-Free Indoor Navigation

The long-term objective of our research is to develop a method for infrastructure-free simultaneous localization and mapping (SLAM) and context recognition for tactical situational awareness. Localization will be realized by propagating motion measurements obtained using a monocular camera, a foot-mounted Inertial Measurement Unit (IMU), sonar, and a barometer. Due to the size and weight requirements set by tactical applications, Micro-Electro-Mechanical (MEMS) sensors will be used. However, MEMS sensors suffer from biases and drift errors that may substantially decrease the position accuracy. Therefore, sophisticated error modelling and implementation of integration algorithms are key for providing a viable result. Algorithms used for multi-sensor fusion have traditionally been different versions of Kalman filters. However, Kalman filters are based on the assumptions that the state propagation and measurement models are linear with additive Gaussian noise. Neither of the assumptions is correct for tactical applications, especially for dismounted soldiers, or rescue personnel. Therefore, error modelling and implementation of advanced fusion algorithms are essential for providing a viable result. Our approach is to use particle filtering (PF), which is a sophisticated option for integrating measurements emerging from pedestrian motion having non-Gaussian error characteristics. This paper discusses the statistical modelling of the measurement errors from inertial sensors and vision based heading and translation measurements to include the correct error probability density functions (pdf) in the particle filter implementation. Then, model fitting is used to verify the pdfs of the measurement errors. Based on the deduced error models of the measurements, particle filtering method is developed to fuse all this information, where the weights of each particle are computed based on the specific models derived. The performance of the developed method is tested via two experiments, one at a university’s premises and another in realistic tactical conditions. The results show significant improvement on the horizontal localization when the measurement errors are carefully modelled and their inclusion into the particle filtering implementation correctly realized

Compact North Finding System

The knowledge of orientation of an object with respect to earth-fixed reference coordinate system is crucial in many applications. For instance, in oil mining it is very crucial to accurately know the orientation of the drilling equipment under the earth surface to drill through desired path. In this context we propose a compact inertial sensor system that estimates the instantaneous orientation of the system using accelerometer and gyroscope-derived tilt and azimuth angles. To keep the system size small, we use two-axis accelerometer and one-axis gyroscope. In addition, to avoid high sensor cost, the sensor biases are removed using indexing method. The proposed system estimates the orientation of the compact system in almost all of the orientations and additionally it also provides the measurement accuracy and integrity values that help in ascertaining the validity of the orientation estimate.acceptedVersionPeer reviewe

Challenges in Arctic Navigation and Geospatial Data : User Perspective and Solutions Roadmap

Navigation and location-based applications, including business such as transport, tourism, and mining, in Arctic areas face a variety of specific challenges. In fact, these challenges concern not only the Arctic Circle but certain other areas as well, such as the Gulf of Bothnia. This report provides a review on these challengs which concern a variety of technologies ranging from satellite navigation to telecommunications and mapping. In order to find out end-users' views on the significance of Arctic challenges, an online survey was conducted. The 77 respondents representing all Arctic countries, the majority being from Finland, highlighted the challenges in telecommunications as well as accuracy concerns for emerging applications dealing with precise navigation. This report provides a review of possible technologies for addressing the Arctic challenges, based on which a road map for solving them is developed. The road map also uses the results of expert working groups from the Challenges in Arctic Navigation workshop arranged in April 2018 in Olos, Muonio, Finland. This report was produced within the ARKKI project. It was funded by the Finnish Ministry of Foreign Affairs under the Baltic Sea, Barents and Arctic cooperation programme, and implemented by the Finnish Geospatial Research Institute in collaboration with the Finnish Ministry of Transport and Communications

Challenges in Arctic Navigation: the User Perspective

This paper underlines the challenges of navigation in the Arctic from the user perspective by means of an online survey. The main target of the survey was to find out the users' views and real-life experiences on the challenges in navigation and geospatial information-based services in the Arctic region. The paper studies relations between the represented industry, encountered challenges and areas of operation. Navigation in the Arctic area and similar circumstances in high latitudes is known to be challenging in terms of weather conditions, lack of services and infrastructure. As the novel technologies, e.g., intelligent transport systems mature, the need for exact and timely geospatial information will increase. According to the results, the most significant challenges are uneven coverage of positioning, untimely weather information, and telecommunication issues. Although the number of respondents was lower than expected (83 complete responses), the results indicate the differences in navigation and location-based services between countries and public versus commercial actors. We found two major dependent variables (nationality and market segment), which are analyzed further. The results suggest guidelines for the future developments of the navigation and positioning services in the high latitudes

Hybridization of GNSS and On-Board Sensors for Validating the Aurora Ecosystem

This paper presents a hybrid navigation algorithm based on loose coupling of the on-board speedometer and inertial sensors of a land vehicle with a GNSS receiver. An Extended Kalman Filter estimating ten error states is used as the hybridization framework. The algorithm is developed to serve as a baseline for the evaluation of the navigation infrastructure of the Aurora ecosystem which is an Arctic test bed for autonomous vehicles and intelligent transport systems. In the experimental tests we focus on the performance of the navigation algorithm during GNSS outages. First, the tests indicate that the quality of GNSS updates has an immediate effect on how fast the position errors accumulate when GNSS becomes unavailable. Second, using low-cost sensors together with the current navigation infrastructure available at the Aurora test site, GNSS position fixes need to be obtained at intervals no longer than 4 seconds in order to maintain a 95 % horizontal positioning accuracy better than 0.2 meters. The results serve as a basis for recommendations for further development of the Aurora ecosystem, suggesting that further positioning infrastructure could be deployed for guaranteeing a navigation performance adequate for autonomous vehicles

Contributions to Positioning Methods on Low-Cost Devices

Global Navigation Satellite System (GNSS) receivers are common in modern consumer devices that make use of position information, e.g., smartphones and personal navigation assistants. With a GNSS receiver, a position solution with an accuracy in the order of five meters is usually available if the reception conditions are benign, but the performance degrades rapidly in less favorable environments and, on the other hand, a better accuracy would be beneficial in some applications. This thesis studies advanced methods for processing the measurements of low-cost devices that can be used for improving the positioning performance. The focus is on GNSS receivers and microelectromechanical (MEMS) inertial sensors which have become common in mobile devices such as smartphones. First, methods to compensate for the additive bias of a MEMS gyroscope are investigated. Both physical slewing of the sensor and mathematical modeling of the bias instability process are considered. The use of MEMS inertial sensors for pedestrian navigation indoors is studied in the context of map matching using a particle filter. A high-sensitivity GNSS receiver is used to produce coarse initialization information for the filter to decrease the computational burden without the need to exploit local building infrastructure. Finally, a cycle slip detection scheme for stand-alone single-frequency GNSS receivers is proposed. Experimental results show that even a MEMS gyroscope can reach an accuracy suitable for North seeking if the measurement errors are carefully modeled and eliminated. Furthermore, it is seen that even a relatively coarse initialization can be adequate for long-term indoor navigation without an excessive computational burden if a detailed map is available. The cycle slip detection results suggest that even small cycle slips can be detected with mass-market GNSS receivers, but the detection rate needs to be improved

Bias Prediction for MEMS Gyroscopes

acceptedVersionPeer reviewe

Effect of Carouseling on Angular Rate Sensor Error Processes

acceptedVersionPeer reviewe