ENGLISH

Statistical Optimization for Geometric Computation: Theory and Practice

By Kenichi Kanatani (Eds.)

Book information

Publisher
Elsevier
Year
1996
ISBN
0444824278, 9780444824271
LCC
TJ211 .K354 1996
Open Library ID
OL963988M
Language
english
Format
DJVU
Filesize
3 MB (3165439 bytes)
Series
Machine intelligence and pattern recognition 18
Pages
1-509\508
Library
Kolxo3
DPI
300
Time added
2009-07-20 03:45:11

Description

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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