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Statements

Subject Item
n2:RIV%2F00216305%3A26220%2F12%3APU97099%21RIV14-MSM-26220___
rdf:type
skos:Concept n14:Vysledek
dcterms:description
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. 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.
dcterms:title
Adapting Polynomial Mahalanobis Distance for Self-Supervised Learning in an Outdoor Environment Adapting Polynomial Mahalanobis Distance for Self-Supervised Learning in an Outdoor Environment
skos:prefLabel
Adapting Polynomial Mahalanobis Distance for Self-Supervised Learning in an Outdoor Environment Adapting Polynomial Mahalanobis Distance for Self-Supervised Learning in an Outdoor Environment
skos:notation
RIV/00216305:26220/12:PU97099!RIV14-MSM-26220___
n14:predkladatel
n15:orjk%3A26220
n3:aktivita
n20:Z
n3:aktivity
Z(MSM0021630529)
n3:dodaniDat
n7:2014
n3:domaciTvurceVysledku
n12:7313373 n12:2980576 n12:3822761
n3:druhVysledku
n11:D
n3:duvernostUdaju
n22:S
n3:entitaPredkladatele
n18:predkladatel
n3:idSjednocenehoVysledku
121032
n3:idVysledku
RIV/00216305:26220/12:PU97099
n3:jazykVysledku
n9:eng
n3:klicovaSlova
Polynomial Mahalanobis Distance, A Three Stage Algorithm, Self-supervised Learning, Robotics
n3:klicoveSlovo
n19:A%20Three%20Stage%20Algorithm n19:Polynomial%20Mahalanobis%20Distance n19:Self-supervised%20Learning n19:Robotics
n3:kontrolniKodProRIV
[A252B0BFA797]
n3:mistoKonaniAkce
Honolulu, Hawaii
n3:mistoVydani
Neuveden
n3:nazevZdroje
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)
n3:obor
n16:JD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:rokUplatneniVysledku
n7:2012
n3:tvurceVysledku
Mikšík, Ondřej Petyovský, Petr Richter, Miloslav
n3:typAkce
n4:WRD
n3:zahajeniAkce
2011-12-18+01:00
n3:zamer
n6:MSM0021630529
s:numberOfPages
6
n17:hasPublisher
The Institute of Electrical and Electronics Engineers, Inc.
n10:isbn
978-1-4577-2134-2
n21:organizacniJednotka
26220