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2016 | OriginalPaper | Chapter

Entropy Maximization of Occupancy Grid Map for Selecting Good Registration of SLAM Algorithms

Authors : Daishiro Akiyama, Kazuya Matsuo, Shuichi Kurogi

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

This paper analyzes entropy of occupancy grid map (OGM) for evaluating registration performance of SLAM (simultaneous localization and mapping) algorithms. So far, there are a number of SLAM algorithms having been proposed, but we do not have general measure to evaluate the registration performance of point clouds obtained by LRF (laser range finder) for SLAM algorithms. This paper analyzes to show that good registration seems corresponding to large overlap of point clouds in OGM as well as large entropy, large uncertainty and low information of OGM. This analysis indicates a method of entropy maximization of OGM for selecting good registration of SLAM algorithms. By means of executing numerical experiments, we show the validity and the effectiveness of the entropy of OGM to evaluate the registration performance.

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Metadata
Title
Entropy Maximization of Occupancy Grid Map for Selecting Good Registration of SLAM Algorithms
Authors
Daishiro Akiyama
Kazuya Matsuo
Shuichi Kurogi
Copyright Year
2016
Book
Neural Information Processing

Print ISBN: 978-3-319-46686-6

Electronic ISBN: 978-3-319-46687-3

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46687-3_17

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