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Geometry and Computing #4: Geometric Algebra with Applications in Engineeringby Christian Perwass
Synopses & Reviews
The application of geometric algebra to the engineering sciences is a young, active subject of research. The promise of this field is that the mathematical structure of geometric algebra together with its descriptive power will result in intuitive and more robust algorithms. This book examines all aspects essential for a successful application of geometric algebra: the theoretical foundations, the representation of geometric constraints, and the numerical estimation from uncertain data. Formally, the book consists of two parts: theoretical foundations and applications. The first part includes chapters on random variables in geometric algebra, linear estimation methods that incorporate the uncertainty of algebraic elements, and the representation of geometry in Euclidean, projective, conformal and conic space. The second part is dedicated to applications of geometric algebra, which include uncertain geometry and transformations, a generalized camera model, and pose estimation. Graduate students, scientists, researchers and practitioners will benefit from this book. The examples given in the text are mostly recent research results, so practitioners can see how to apply geometric algebra to real tasks, while researchers note starting points for future investigations. Students will profit from the detailed introduction to geometric algebra, while the text is supported by the author's visualization software, CLUCalc, freely available online, and a website that includes downloadable exercises, slides and tutorials.
This book examines all aspects essential for a successful application of geometric algebra to engineering--the theoretical foundations, the representation of geometric constraints, and the numerical estimation from uncertain data.
Table of Contents
Introduction.- Learning Geometric Algebra with CLUCalc.- Algebra.- Geometries.- Numerics.- Uncertain Geometric Entities and Operators.- The Inversion Camera Model.- Monocular Pose Estimation.- Versor Functions.- Random Variable Space.
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