Mitigation of odometry drift with a single ranging link in GNSS-limited environments

Vision-based systems can estimate the vehicle's positions and attitude with a low cost and simple implementation, but the performance is very sensitive to environmental conditions. Moreover, estimation errors are accumulated without a bound since visual odometry is a dead-reckoning process. To improve the robustness to environmental conditions, vision-based systems can be augmented with inertial sensors, and the loop closing technique can be applied to reduce the drift. However, only with on-board sensors, vehicle's poses can only be estimated in a local navigation frame, which is randomly defined for each mission. To obtain globally-referred poses, absolute position estimates obtained with GNSS can be fused with on-board measurements (obtained with either vision-only or visual-inertial odometry). However, in many cases (e.g. urban canyons, indoor environments), GNSS-based positioning is unreliable or entirely unavailable due to signal interruptions and blocking, while we can still obtain ranging links from various sources, such as signals of opportunity or low cost radio-based ranging modules. We propose a graph-based data fusion method of the on-board odometry data and ranging measurements to mitigate pose drifts in environments where GNSS-based positioning is unavailable. The proposed algorithm is evaluated both with synthetic and real data

Statistical Filtering for Multimodal Mobility Modeling in Cyber Physical Systems

A Cyber-Physical System integrates computations and dynamics of physical processes. It is an engineering discipline focused on technology with a strong foundation in mathematical abstractions. It shares many of these abstractions with engineering and computer science, but still requires adaptation to suit the dynamics of the physical world. In such a dynamic system, mobility management is one of the key issues against developing a new service. For example, in the study of a new mobile network, it is necessary to simulate and evaluate a protocol before deployment in the system. Mobility models characterize mobile agent movement patterns. On the other hand, they describe the conditions of the mobile services. The focus of this thesis is on mobility modeling in cyber-physical systems. A macroscopic model that captures the mobility of individuals (people and vehicles) can facilitate an unlimited number of applications. One fundamental and obvious example is traffic profiling. Mobility in most systems is a dynamic process and small non-linearities can lead to substantial errors in the model. Extensive research activities on statistical inference and filtering methods for data modeling in cyber-physical systems exist. In this thesis, several methods are employed for multimodal data fusion, localization and traffic modeling. A novel energy-aware sparse signal processing method is presented to process massive sensory data. At baseline, this research examines the application of statistical filters for mobility modeling and assessing the difficulties faced in fusing massive multi-modal sensory data. A statistical framework is developed to apply proposed methods on available measurements in cyber-physical systems. The proposed methods have employed various statistical filtering schemes (i.e., compressive sensing, particle filtering and kernel-based optimization) and applied them to multimodal data sets, acquired from intelligent transportation systems, wireless local area networks, cellular networks and air quality monitoring systems. Experimental results show the capability of these proposed methods in processing multimodal sensory data. It provides a macroscopic mobility model of mobile agents in an energy efficient way using inconsistent measurements