Date of Award

Spring 5-2016

Embargo Period

4-3-2017

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Robotics Institute

Advisor(s)

William L. Whittaker

Abstract

This research addresses the modeling of substantially 3D planetary terrain features, such as skylights, canyons, craters, rocks, and mesas, by a surface robot. The sun lights planetary features with transient, directional illumination. For concave features like skylight pits, craters, and canyons, this can lead to dark shadows. For all terrain features, the ability to detect interest points and to match them between images is complicated by changing illumination, so seeing planetary features in the best light requires a coordinated dance with the sun as it arcs across the sky. The research develops a process for planned-view-trajectory model building that converts a coarse model of a terrain feature and knowledge about illumination change and mission parameters into a view trajectory planning problem, plans and executes a view trajectory, and builds a detailed model from the captured images. An understanding of how view and illumination angles a ect model quality is reached through controlled lighting laboratory experiments. The planning of view trajectories, (what to image, from where, and at what time), is cast as an OPTWIND, a new vehicle routing problem formulated in the thesis work. Each part of the planned-view-trajectory model building process is examined in detail, with existing tools identified and tested to solve parts of the problem, where appropriate, and new solutions implemented otherwise. The research also demonstrates planned-view-trajectory model building. Contributions of the research include development, implementation, and demonstration of planned-view-trajectory model building, experimental determination of factors a ecting model quality and formulation of view trajectory planning as a new vehicle routing problem. Datasets for planetary analog terrain and for imaging under directional lighting were also collected, totaling over 25,000 images.

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