Self-diagnosis of localization status for autonomous mobile robots

Jiwoong Kim, Jooyoung Park, Woo Jin Chung

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

It is essential to provide reliable localization results to allow mobile robots to navigate autonomously. Even though many state-of-the-art localization schemes have so far shown satisfactory performance in various environments, localization has still been difficult under specific conditions, such as extreme environmental changes. Since many robots cannot diagnose for themselves whether the localization results are reliable, there can be serious autonomous navigation problems. To solve this problem, this study proposes a self-diagnosis scheme for the localization status. In this study, two indicators are empirically defined for the self-diagnosis of localization status. Each indicator shows significant changes when there are difficulties in light detection and ranging (LiDAR) sensor-based localization. In addition, the classification model of localization status is trained through machine learning using the two indicators. A robot can diagnose the localization status itself using the proposed classification model. To verify the usefulness of the proposed method, we carried out localization experiments in real environments. The proposed classification model successfully detected situations where the localization accuracy is significantly degraded due to extreme environmental changes.

Original languageEnglish
Article number3168
JournalSensors (Switzerland)
Volume18
Issue number9
DOIs
Publication statusPublished - 2018 Sep 19

Fingerprint

robots
Mobile robots
Robots
Learning systems
Navigation
Light
Sensors
autonomous navigation
machine learning
Experiments
sensors

Keywords

  • Failure detection
  • Light detection and ranging sensor
  • Localization
  • Mobile robot
  • Support vector machine

ASJC Scopus subject areas

  • Analytical Chemistry
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

Cite this

Self-diagnosis of localization status for autonomous mobile robots. / Kim, Jiwoong; Park, Jooyoung; Chung, Woo Jin.

In: Sensors (Switzerland), Vol. 18, No. 9, 3168, 19.09.2018.

Research output: Contribution to journalArticle

@article{cf885219521d46a5a659194cad274029,
title = "Self-diagnosis of localization status for autonomous mobile robots",
abstract = "It is essential to provide reliable localization results to allow mobile robots to navigate autonomously. Even though many state-of-the-art localization schemes have so far shown satisfactory performance in various environments, localization has still been difficult under specific conditions, such as extreme environmental changes. Since many robots cannot diagnose for themselves whether the localization results are reliable, there can be serious autonomous navigation problems. To solve this problem, this study proposes a self-diagnosis scheme for the localization status. In this study, two indicators are empirically defined for the self-diagnosis of localization status. Each indicator shows significant changes when there are difficulties in light detection and ranging (LiDAR) sensor-based localization. In addition, the classification model of localization status is trained through machine learning using the two indicators. A robot can diagnose the localization status itself using the proposed classification model. To verify the usefulness of the proposed method, we carried out localization experiments in real environments. The proposed classification model successfully detected situations where the localization accuracy is significantly degraded due to extreme environmental changes.",
keywords = "Failure detection, Light detection and ranging sensor, Localization, Mobile robot, Support vector machine",
author = "Jiwoong Kim and Jooyoung Park and Chung, {Woo Jin}",
year = "2018",
month = "9",
day = "19",
doi = "10.3390/s18093168",
language = "English",
volume = "18",
journal = "Sensors (Switzerland)",
issn = "1424-8220",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "9",

}

TY - JOUR

T1 - Self-diagnosis of localization status for autonomous mobile robots

AU - Kim, Jiwoong

AU - Park, Jooyoung

AU - Chung, Woo Jin

PY - 2018/9/19

Y1 - 2018/9/19

N2 - It is essential to provide reliable localization results to allow mobile robots to navigate autonomously. Even though many state-of-the-art localization schemes have so far shown satisfactory performance in various environments, localization has still been difficult under specific conditions, such as extreme environmental changes. Since many robots cannot diagnose for themselves whether the localization results are reliable, there can be serious autonomous navigation problems. To solve this problem, this study proposes a self-diagnosis scheme for the localization status. In this study, two indicators are empirically defined for the self-diagnosis of localization status. Each indicator shows significant changes when there are difficulties in light detection and ranging (LiDAR) sensor-based localization. In addition, the classification model of localization status is trained through machine learning using the two indicators. A robot can diagnose the localization status itself using the proposed classification model. To verify the usefulness of the proposed method, we carried out localization experiments in real environments. The proposed classification model successfully detected situations where the localization accuracy is significantly degraded due to extreme environmental changes.

AB - It is essential to provide reliable localization results to allow mobile robots to navigate autonomously. Even though many state-of-the-art localization schemes have so far shown satisfactory performance in various environments, localization has still been difficult under specific conditions, such as extreme environmental changes. Since many robots cannot diagnose for themselves whether the localization results are reliable, there can be serious autonomous navigation problems. To solve this problem, this study proposes a self-diagnosis scheme for the localization status. In this study, two indicators are empirically defined for the self-diagnosis of localization status. Each indicator shows significant changes when there are difficulties in light detection and ranging (LiDAR) sensor-based localization. In addition, the classification model of localization status is trained through machine learning using the two indicators. A robot can diagnose the localization status itself using the proposed classification model. To verify the usefulness of the proposed method, we carried out localization experiments in real environments. The proposed classification model successfully detected situations where the localization accuracy is significantly degraded due to extreme environmental changes.

KW - Failure detection

KW - Light detection and ranging sensor

KW - Localization

KW - Mobile robot

KW - Support vector machine

UR - http://www.scopus.com/inward/record.url?scp=85053919009&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85053919009&partnerID=8YFLogxK

U2 - 10.3390/s18093168

DO - 10.3390/s18093168

M3 - Article

C2 - 30235883

AN - SCOPUS:85053919009

VL - 18

JO - Sensors (Switzerland)

JF - Sensors (Switzerland)

SN - 1424-8220

IS - 9

M1 - 3168

ER -