Local visual homing navigation using gradient-descent learning of haar-like features

Man Dong Kim, Daeeun Kim

Research output: Contribution to journalArticle

Abstract

The autonomous mobile technology of mobile robots has been developed. Visual navigation is one of non-trivial problems and it has been tackled with biologically inspired models. Especially, ant navigation system inspires robot navigation. The visual cell structure of ants was modeled with Haar-like features. Those features can be obtained with computationally efficient process. In this paper, we handle visual homing navigation where an agent is supposed to return home after exploration in the environment. We apply a learning process based on gradient-descent algorithm to estimate the homing vector at an arbitrary position of a mobile agent. Our approach is simple but very effective to find the homing vector and its performance is better than the conventional algorithm. From our results, the Haar-like features in the snapshot images are sufficient to estimate the homing vector.

Original languageEnglish
Pages (from-to)1244-1251
Number of pages8
JournalTransactions of the Korean Institute of Electrical Engineers
Volume68
Issue number10
DOIs
Publication statusPublished - 2019 Jan 1

Fingerprint

Navigation
Mobile agents
Navigation systems
Mobile robots
Robots

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

@article{d6c3c2e5ff6846be93a42342b2e61ac9,
title = "Local visual homing navigation using gradient-descent learning of haar-like features",
abstract = "The autonomous mobile technology of mobile robots has been developed. Visual navigation is one of non-trivial problems and it has been tackled with biologically inspired models. Especially, ant navigation system inspires robot navigation. The visual cell structure of ants was modeled with Haar-like features. Those features can be obtained with computationally efficient process. In this paper, we handle visual homing navigation where an agent is supposed to return home after exploration in the environment. We apply a learning process based on gradient-descent algorithm to estimate the homing vector at an arbitrary position of a mobile agent. Our approach is simple but very effective to find the homing vector and its performance is better than the conventional algorithm. From our results, the Haar-like features in the snapshot images are sufficient to estimate the homing vector.",
author = "Kim, {Man Dong} and Daeeun Kim",
year = "2019",
month = "1",
day = "1",
doi = "10.5370/KIEE.2019.68.10.1244",
language = "English",
volume = "68",
pages = "1244--1251",
journal = "Transactions of the Korean Institute of Electrical Engineers",
issn = "1975-8359",
publisher = "Korean Institute of Electrical Engineers",
number = "10",

}

Local visual homing navigation using gradient-descent learning of haar-like features. / Kim, Man Dong; Kim, Daeeun.

In: Transactions of the Korean Institute of Electrical Engineers, Vol. 68, No. 10, 01.01.2019, p. 1244-1251.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Local visual homing navigation using gradient-descent learning of haar-like features

AU - Kim, Man Dong

AU - Kim, Daeeun

PY - 2019/1/1

Y1 - 2019/1/1

N2 - The autonomous mobile technology of mobile robots has been developed. Visual navigation is one of non-trivial problems and it has been tackled with biologically inspired models. Especially, ant navigation system inspires robot navigation. The visual cell structure of ants was modeled with Haar-like features. Those features can be obtained with computationally efficient process. In this paper, we handle visual homing navigation where an agent is supposed to return home after exploration in the environment. We apply a learning process based on gradient-descent algorithm to estimate the homing vector at an arbitrary position of a mobile agent. Our approach is simple but very effective to find the homing vector and its performance is better than the conventional algorithm. From our results, the Haar-like features in the snapshot images are sufficient to estimate the homing vector.

AB - The autonomous mobile technology of mobile robots has been developed. Visual navigation is one of non-trivial problems and it has been tackled with biologically inspired models. Especially, ant navigation system inspires robot navigation. The visual cell structure of ants was modeled with Haar-like features. Those features can be obtained with computationally efficient process. In this paper, we handle visual homing navigation where an agent is supposed to return home after exploration in the environment. We apply a learning process based on gradient-descent algorithm to estimate the homing vector at an arbitrary position of a mobile agent. Our approach is simple but very effective to find the homing vector and its performance is better than the conventional algorithm. From our results, the Haar-like features in the snapshot images are sufficient to estimate the homing vector.

UR - http://www.scopus.com/inward/record.url?scp=85074370277&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85074370277&partnerID=8YFLogxK

U2 - 10.5370/KIEE.2019.68.10.1244

DO - 10.5370/KIEE.2019.68.10.1244

M3 - Article

AN - SCOPUS:85074370277

VL - 68

SP - 1244

EP - 1251

JO - Transactions of the Korean Institute of Electrical Engineers

JF - Transactions of the Korean Institute of Electrical Engineers

SN - 1975-8359

IS - 10

ER -