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

Small Unmanned Aircraft System for Pavement Inspection: Task 4\u2014Execute the Field Demonstration Plan and Analyze the Collected Data

The primary objectives of this research project are to develop recommended processes and procedures for using small unmanned/uncrewed aircraft system (sUAS) to complement current methods of airport Pavement Management Program (PMP) inspections and to evaluate various types of sUAS platforms and sensors that will lead to recommended minimum specifications required for consistently safe, reliable, and effective sUAS-assisted airport PMP inspections. Under Task 4, the research team developed and executed field demonstrated plans to safely deploy several sUAS at six airports in Michigan, Illinois, Iowa, and New Jersey from December 2020 to August 2021. Red, green, and blue (RGB) optical orthophotos, digital elevation models (DEMs), hillshades derived from DEMs, and thermal orthophotos collected using several sUAS at different altitudes were analyzed for their usefulness in airfield distress detection. Based on the data analyses and results, RGB orthophotos of 1.5 mm/pixel and DEMs of 6 mm/pixel resolution, or higher, are highly recommended for airfield pavement distress detection and rating

The grading inspection of an agricultural product: decision-making problems and strategies with their training and selection implications

This research thesis describes an investigation into the grading inspection of apples with particular reference to the decision-making component of the inspection task. The research commences with an evaluation, conducted across seven grading packhouses in the United Kingdom, of the correctness and consistency with which examiners judge and classify fruit in accordance with the European Economic Community Standards and attempts to broadly answer two questions: (i) How well do human inspectors of apples perform under optimum conditions (the decision task with trivial search)? and (ii) how well do human inspectors of apples perform under actual 'on-line' conditions (the decision task with active search)? Subsequent analysis identifies those factors contributing to poor decision-making performance, of which four are the subject of further investigation. These are inspector training, selection of inspectors, the deployment of inspectors, and the method of presentation of fruit. [Continues.