Adapting Polynomial Mahalanobis Distance for Self-Supervised Learning in an Outdoor Environment
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F12%3APU97099" target="_blank" >RIV/00216305:26220/12:PU97099 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—
Alternative languages
Result language
angličtina
Original language name
Adapting Polynomial Mahalanobis Distance for Self-Supervised Learning in an Outdoor Environment
Original language 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 PolynomialMahalanobis 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 represent
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
—
Result continuities
Project
—
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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)
ISBN
978-1-4577-2134-2
ISSN
—
e-ISSN
—
Number of pages
6
Pages from-to
448-453
Publisher name
The Institute of Electrical and Electronics Engineers, Inc.
Place of publication
Neuveden
Event location
Honolulu, Hawaii
Event date
Dec 18, 2011
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—