Predictive parameter estimation for Bayesian filtering

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 113-117).In this thesis, I develop CELLO, an algorithm for predicting the covariances of any Gaussian model used to account for uncertainty in a complex system. The primary motivation for this work is state estimation; often, complex raw sensor measurements are processed into low dimensional observations of a vehicle state. I argue that the covariance of these observations can be well-modelled as a function of the raw sensor measurement, and provide a method to learn this function from data. This method is computationally cheap, asymptotically correct, easy to extend to new sensors, and noninvasive, in the sense that it augments, rather than disrupts, existing filtering algorithms. I additionally present two important variants; first, I extend CELLO to learn even when ground truth vehicle states are unavailable; and second, I present an equivalent Bayesian algorithm. I then use CELLO to learn covariance models for several systems, including a laser scan-matcher, an optical flow system, and a visual odometry system. I show that filtering using covariances predicted by CELLO can quantitatively improve estimator accuracy and consistency, both relative to a fixed covariance model and relative to carefully tuned domain-specific covariance models.by William Vega-Brown.S.M

Uncertainty Minimization in Robotic 3D Mapping Systems Operating in Dynamic Large-Scale Environments

This dissertation research is motivated by the potential and promise of 3D sensing technologies in safety and security applications. With specific focus on unmanned robotic mapping to aid clean-up of hazardous environments, under-vehicle inspection, automatic runway/pavement inspection and modeling of urban environments, we develop modular, multi-sensor, multi-modality robotic 3D imaging prototypes using localization/navigation hardware, laser range scanners and video cameras. While deploying our multi-modality complementary approach to pose and structure recovery in dynamic real-world operating conditions, we observe several data fusion issues that state-of-the-art methodologies are not able to handle. Different bounds on the noise model of heterogeneous sensors, the dynamism of the operating conditions and the interaction of the sensing mechanisms with the environment introduce situations where sensors can intermittently degenerate to accuracy levels lower than their design specification. This observation necessitates the derivation of methods to integrate multi-sensor data considering sensor conflict, performance degradation and potential failure during operation. Our work in this dissertation contributes the derivation of a fault-diagnosis framework inspired by information complexity theory to the data fusion literature. We implement the framework as opportunistic sensing intelligence that is able to evolve a belief policy on the sensors within the multi-agent 3D mapping systems to survive and counter concerns of failure in challenging operating conditions. The implementation of the information-theoretic framework, in addition to eliminating failed/non-functional sensors and avoiding catastrophic fusion, is able to minimize uncertainty during autonomous operation by adaptively deciding to fuse or choose believable sensors. We demonstrate our framework through experiments in multi-sensor robot state localization in large scale dynamic environments and vision-based 3D inference. Our modular hardware and software design of robotic imaging prototypes along with the opportunistic sensing intelligence provides significant improvements towards autonomous accurate photo-realistic 3D mapping and remote visualization of scenes for the motivating applications

Proceedings of the Mobile Satellite Conference

A satellite-based mobile communications system provides voice and data communications to mobile users over a vast geographic area. The technical and service characteristics of mobile satellite systems (MSSs) are presented and form an in-depth view of the current MSS status at the system and subsystem levels. Major emphasis is placed on developments, current and future, in the following critical MSS technology areas: vehicle antennas, networking, modulation and coding, speech compression, channel characterization, space segment technology and MSS experiments. Also, the mobile satellite communications needs of government agencies are addressed, as is the MSS potential to fulfill them