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Detail publikace
RICHTER, M. PETYOVSKÝ, P. MIKŠÍK, O.
Originální název
Adapting Polynomial Mahalanobis Distance for Self-Supervised Learning in an Outdoor Environment
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
This paper addresses the problem of autonomous navigation of UGV in an unstructured environment. Generally, state-of-the-art approaches use color based segmentation of road/non-road regions in particular. There arises an important question, how is the distance between an input pixel and a color model measured. Many algorithms employ Mahalanobis distance, since Mahalanobis distance better follows the data distribution, however it is assumed, that the data points have a normal distribution. Recently proposed Polynomial Mahalanobis Distance (PMD) represents more discriminative metric, which provides superior results in an unstructured terrain, especially, if the road is barely visible even for humans. In this paper, we discuss properties of the Polynomial Mahalanobis Distance, and propose a novel framework - A Three Stage Algorithm (TSA), which deals with both, picking of suitable data points from the training area as well as self-supervised learning algorithm for long-term road representation.
Klíčová slova
Polynomial Mahalanobis Distance, A Three Stage Algorithm, Self-supervised Learning, Robotics
Autoři
RICHTER, M.; PETYOVSKÝ, P.; MIKŠÍK, O.
Rok RIV
2012
Vydáno
3. 1. 2012
Nakladatel
The Institute of Electrical and Electronics Engineers, Inc.
ISBN
978-1-4577-2134-2
Kniha
Proceedings, The 10th International Conference on Machine Learning and Applications, ICMLA 2011, Volume 1: Main Conference (ISBN 978-1-4577-2134-2 , 978-0-7695-4607-0)
Strany od
448
Strany do
453
Strany počet
6
BibTex
@inproceedings{BUT89729, author="Miloslav {Richter} and Petr {Petyovský} and Ondřej {Mikšík}", title="Adapting Polynomial Mahalanobis Distance for Self-Supervised Learning in an Outdoor Environment", booktitle="Proceedings, The 10th International Conference on Machine Learning and Applications, ICMLA 2011, Volume 1: Main Conference (ISBN 978-1-4577-2134-2 , 978-0-7695-4607-0)", year="2012", pages="448--453", publisher="The Institute of Electrical and Electronics Engineers, Inc.", isbn="978-1-4577-2134-2" }