Active Multiview Perception for Mobile Sensors

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Imaging sensors, including sonar, electro-optical sensors, and LIDAR, are broadly used in mobile robotics for object recognition and 3D reconstruction tasks. Because only part of an object may be visible to a sensor at any given time, successful reconstruction and object classification often requires gathering and fusing images from multiple disparate viewpoints. Mobile robot operations are often energy and time-constrained. Therefore, imaging viewpoints must be planned judiciously to improve target sensing performance while avoiding time-consuming operations. This dissertation presents the development of several novel multiview target classification, reconstruction, and sensor path planning algorithms for imaging sensors. Specifically, a recursive Bayesian estimation method is used for multiview underwater target classification. The expected confidence level (ECL) metric is developed to select the most informative future viewpoints for the sensor. Experiments show that informative path planning algorithms using ECL outperform existing path planning methods for underwater multiview target classification. To address the issue of limited data pertaining to underwater environments, this dissertation introduces a novel method to generate underwater object imagery that is acoustically compliant with that generated by side-scan sonar using the Unreal Engine. This method provides visual approximations for acoustic effects such as back-scatter noise and acoustic shadow while allowing fast rendering with C++ actors in UE for maximizing the size of potential ATR training datasets. In addition, this dissertation presents a novel framework leveraging a generative model for energy-efficient, physics-constrained multiview target reconstruction and sensor path planning. Using an existing generative model to predict or complete an object geometry online future sensor views can be predicted, evaluated, and optimized subject to time and energy demands. Simulation results show that this approach outperforms state-of-the-art algorithms, such as a reinforcement learning approach, ScanRL. It is also shown that the estimated ground truth from the generative model enables other functionalities of the active reconstruction algorithms, such as anomaly or fault detection via visual inspection.

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Active perception; Generative model; Imaging sensor; Next best view; Reconstruction; Viewpoint planning


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Ferrari, Silvia

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MacMartin, Douglas
Weinberger, Kilian

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Mechanical Engineering

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Ph. D., Mechanical Engineering

Degree Level

Doctor of Philosophy

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dissertation or thesis

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