Path Planner for Keyframe-based Visual Autonomous Navigation

Min Gyung Jang; Hee Won Chae; Jae Bok Song

doi:10.23919/ICCAS47443.2019.8971507

Path Planner for Keyframe-based Visual Autonomous Navigation

Min Gyung Jang, Hee Won Chae, Jae Bok Song

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Visual navigation systems have received much attention in recent years. Such systems generate keyframes storing the sensor information that provides a way to correct the robot pose. However, the conventional gradient path does not generate a path that tracks the keyframes since it is based only on the costs regarding the environment. In this study, we propose a keyframe vector-based path planner (KVPP) that is more suitable for visual navigation systems as this path follows more keyframes in the map to increase the chance of keyframe-based pose correction during autonomous driving. This KVPP path uses the existing keyframes as a reference to path generation. Various experiments were conducted to evaluate the KVPP in the real environment and were compared with the conventional gradient path to verify its effectiveness.

Original language	English
Title of host publication	ICCAS 2019 - 2019 19th International Conference on Control, Automation and Systems, Proceedings
Publisher	IEEE Computer Society
Pages	1050-1053
Number of pages	4
ISBN (Electronic)	9788993215182
DOIs	https://doi.org/10.23919/ICCAS47443.2019.8971507
Publication status	Published - 2019 Oct
Event	19th International Conference on Control, Automation and Systems, ICCAS 2019 - Jeju, Korea, Republic of Duration: 2019 Oct 15 → 2019 Oct 18

Publication series

Name	International Conference on Control, Automation and Systems
Volume	2019-October
ISSN (Print)	1598-7833

Conference

Conference	19th International Conference on Control, Automation and Systems, ICCAS 2019
Country/Territory	Korea, Republic of
City	Jeju
Period	19/10/15 → 19/10/18

Bibliographical note

Funding Information:
This work was supported by IITP grant funded by the Korea Government MSIT. (o. 20N 18-0-00622)

Publisher Copyright:
© 2019 Institute of Control, Robotics and Systems - ICROS.

Keywords

Gradient method
Keyframes
Path planner

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.23919/ICCAS47443.2019.8971507

Cite this

Jang, M. G., Chae, H. W., & Song, J. B. (2019). Path Planner for Keyframe-based Visual Autonomous Navigation. In ICCAS 2019 - 2019 19th International Conference on Control, Automation and Systems, Proceedings (pp. 1050-1053). Article 8971507 (International Conference on Control, Automation and Systems; Vol. 2019-October). IEEE Computer Society. https://doi.org/10.23919/ICCAS47443.2019.8971507

Path Planner for Keyframe-based Visual Autonomous Navigation. / Jang, Min Gyung; Chae, Hee Won; Song, Jae Bok.
ICCAS 2019 - 2019 19th International Conference on Control, Automation and Systems, Proceedings. IEEE Computer Society, 2019. p. 1050-1053 8971507 (International Conference on Control, Automation and Systems; Vol. 2019-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jang, MG, Chae, HW & Song, JB 2019, Path Planner for Keyframe-based Visual Autonomous Navigation. in ICCAS 2019 - 2019 19th International Conference on Control, Automation and Systems, Proceedings., 8971507, International Conference on Control, Automation and Systems, vol. 2019-October, IEEE Computer Society, pp. 1050-1053, 19th International Conference on Control, Automation and Systems, ICCAS 2019, Jeju, Korea, Republic of, 19/10/15. https://doi.org/10.23919/ICCAS47443.2019.8971507

@inproceedings{2f8ab32c67cf4c40909505f8d85c207f,

title = "Path Planner for Keyframe-based Visual Autonomous Navigation",

abstract = "Visual navigation systems have received much attention in recent years. Such systems generate keyframes storing the sensor information that provides a way to correct the robot pose. However, the conventional gradient path does not generate a path that tracks the keyframes since it is based only on the costs regarding the environment. In this study, we propose a keyframe vector-based path planner (KVPP) that is more suitable for visual navigation systems as this path follows more keyframes in the map to increase the chance of keyframe-based pose correction during autonomous driving. This KVPP path uses the existing keyframes as a reference to path generation. Various experiments were conducted to evaluate the KVPP in the real environment and were compared with the conventional gradient path to verify its effectiveness.",

keywords = "Gradient method, Keyframes, Path planner",

author = "Jang, {Min Gyung} and Chae, {Hee Won} and Song, {Jae Bok}",

note = "Funding Information: This work was supported by IITP grant funded by the Korea Government MSIT. (o. 20N 18-0-00622) Publisher Copyright: {\textcopyright} 2019 Institute of Control, Robotics and Systems - ICROS.; 19th International Conference on Control, Automation and Systems, ICCAS 2019 ; Conference date: 15-10-2019 Through 18-10-2019",

year = "2019",

month = oct,

doi = "10.23919/ICCAS47443.2019.8971507",

language = "English",

series = "International Conference on Control, Automation and Systems",

publisher = "IEEE Computer Society",

pages = "1050--1053",

booktitle = "ICCAS 2019 - 2019 19th International Conference on Control, Automation and Systems, Proceedings",

}

TY - GEN

T1 - Path Planner for Keyframe-based Visual Autonomous Navigation

AU - Jang, Min Gyung

AU - Chae, Hee Won

AU - Song, Jae Bok

PY - 2019/10

Y1 - 2019/10

N2 - Visual navigation systems have received much attention in recent years. Such systems generate keyframes storing the sensor information that provides a way to correct the robot pose. However, the conventional gradient path does not generate a path that tracks the keyframes since it is based only on the costs regarding the environment. In this study, we propose a keyframe vector-based path planner (KVPP) that is more suitable for visual navigation systems as this path follows more keyframes in the map to increase the chance of keyframe-based pose correction during autonomous driving. This KVPP path uses the existing keyframes as a reference to path generation. Various experiments were conducted to evaluate the KVPP in the real environment and were compared with the conventional gradient path to verify its effectiveness.

AB - Visual navigation systems have received much attention in recent years. Such systems generate keyframes storing the sensor information that provides a way to correct the robot pose. However, the conventional gradient path does not generate a path that tracks the keyframes since it is based only on the costs regarding the environment. In this study, we propose a keyframe vector-based path planner (KVPP) that is more suitable for visual navigation systems as this path follows more keyframes in the map to increase the chance of keyframe-based pose correction during autonomous driving. This KVPP path uses the existing keyframes as a reference to path generation. Various experiments were conducted to evaluate the KVPP in the real environment and were compared with the conventional gradient path to verify its effectiveness.

KW - Gradient method

KW - Keyframes

KW - Path planner

UR - http://www.scopus.com/inward/record.url?scp=85079090286&partnerID=8YFLogxK

U2 - 10.23919/ICCAS47443.2019.8971507

DO - 10.23919/ICCAS47443.2019.8971507

M3 - Conference contribution

AN - SCOPUS:85079090286

T3 - International Conference on Control, Automation and Systems

SP - 1050

EP - 1053

BT - ICCAS 2019 - 2019 19th International Conference on Control, Automation and Systems, Proceedings

PB - IEEE Computer Society

T2 - 19th International Conference on Control, Automation and Systems, ICCAS 2019

Y2 - 15 October 2019 through 18 October 2019

ER -

Path Planner for Keyframe-based Visual Autonomous Navigation

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this