ORCID Identifier(s)

0000-0002-9922-5562

Graduation Semester and Year

2018

Language

English

Document Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Computer Science and Engineering

First Advisor

Manfred Huber

Abstract

With the advent of autonomous agents becoming prominent in everyday lives, the importance of processing the surroundings into understandable features becomes more and more important. 3D point clouds play a major role in the perception of such agents and thus having the ability to correctly decipher features from point clouds is crucial to the planning of actions that the agent would need to undertake. This thesis analyzes holes found in point clouds. Based on two approaches that center around topological data analysis and local point set features respectively. It studies how each of the methods works and how a combination of the two can be used to ascertain important information that may not have been obtainable from just one of them. Moreover, it studies how distinctions between different types of holes in point clouds can be made. The thesis contributes in two ways in the feature extraction from point cloud holes. The first contribution is the constriction of the minimal 1-cycle generated by the addition of edges to the minimum spanning graph generated. These edges are detected using local surface geometry for the points and allow elimination of vertices from the hole boundary thus providing a tighter hole boundary. The second contribution is the classification of the type of hole whose boundary has been detected. This involves calculating a normal to the surface approximated by the boundary and detecting a chain of vertices on the boundary whose surface normal are either orthogonal or parallel to the normal of the boundary points. This thesis approaches the abstract notion of a hole and tries to provide a boundary in order to allow for planning of actions that might involve it, such as determination of further sensing actions or determination of interaction points for object manipulation. We have provided algorithms that calculate the necessary features and have provided results that show their effectiveness in real-world scenarios.

Keywords

Point cloud, Holes, Boundaries, Classification, Topological data analysis, Point set features

Disciplines

Computer Sciences | Physical Sciences and Mathematics

Comments

Degree granted by The University of Texas at Arlington

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