Path Planner for Keyframe-based Visual Autonomous Navigation

Min Gyung Jang, Hee Won Chae, Jae Bok Song

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationICCAS 2019 - 2019 19th International Conference on Control, Automation and Systems, Proceedings
PublisherIEEE Computer Society
Pages1050-1053
Number of pages4
ISBN (Electronic)9788993215182
DOIs
Publication statusPublished - 2019 Oct
Event19th International Conference on Control, Automation and Systems, ICCAS 2019 - Jeju, Korea, Republic of
Duration: 2019 Oct 152019 Oct 18

Publication series

NameInternational Conference on Control, Automation and Systems
Volume2019-October
ISSN (Print)1598-7833

Conference

Conference19th International Conference on Control, Automation and Systems, ICCAS 2019
CountryKorea, Republic of
CityJeju
Period19/10/1519/10/18

Keywords

  • Gradient method
  • Keyframes
  • Path planner

ASJC Scopus subject areas

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

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  • 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). [8971507] (International Conference on Control, Automation and Systems; Vol. 2019-October). IEEE Computer Society. https://doi.org/10.23919/ICCAS47443.2019.8971507