Statistical optimization for geometric computation. Theory and practice by Kenichi Kanatani

Statistical optimization for geometric computation. Theory and practice



Download Statistical optimization for geometric computation. Theory and practice




Statistical optimization for geometric computation. Theory and practice Kenichi Kanatani
Language: English
Page: 508
Format: djvu
ISBN: 0444824278, 9780444824271
Publisher: Elsevier Science Ltd

This book discusses mathematical foundations of statistical inference for building a 3-D model of the environment from image and sensor data that contain noise - a central task for autonomous robots guided by video cameras and sensors. A theoretical accuracy bound is derived for the optimization procedure for maximizing the reliability of the estimation based on noisy data, and practical computational schemes that attain that bound are derived. Many synthetic and real data examples are given to demonstrate that conventional methods are not optimal and how accuracy improves if truly optimal methods are employed.

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