A new architecture for simultaneous localization and mapping: an application of a planetary rover

Kuo Kun Tseng; Jun Li; Yachin Chang; K. L. Yung; C. Y. Chan; Chih Yu Hsu

doi:10.1080/17517575.2019.1698772

A new architecture for simultaneous localization and mapping: an application of a planetary rover

Kuo Kun Tseng
, Jun Li
, Yachin Chang
, K. L. Yung
, C. Y. Chan
, Chih Yu Hsu

Research output: Journal article publication › Journal article › Academic research › peer-review

12 Citations (Scopus)

Abstract

A new architecture implements one Monocular Simultaneous Localization and Mapping (SLAM) system to track the unconstraint motion of a mobile robot. The modified ORB (Oriented FAST and Rotated BRIEF) features represent the landmarks for designing a grid feature detection algorithm. An upgraded feature matching method has improved the robustness of feature matching. The Modified coVariance Extended Kalman Filter (MVEKF) estimates the multiple dimension states of the free moving visual sensor instead of the familiar Extended Kalman Filter (EKF). The simulation navigation of Lunar and Mars surfaces proves that the proposed method is robust and efficient.

Original language	English
Pages (from-to)	1162-1178
Number of pages	17
Journal	Enterprise Information Systems
Volume	15
Issue number	8
DOIs	https://doi.org/10.1080/17517575.2019.1698772
Publication status	Published - 8 Dec 2019

Keywords

extended kalman filter
motion estimation
ORB
planetary rover
simultaneous localisation and mapping
System design

ASJC Scopus subject areas

Computer Science Applications
Information Systems and Management

More information

10.1080/17517575.2019.1698772

http://hdl.handle.net/10397/94620

Cite this

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title = "A new architecture for simultaneous localization and mapping: an application of a planetary rover",

abstract = "A new architecture implements one Monocular Simultaneous Localization and Mapping (SLAM) system to track the unconstraint motion of a mobile robot. The modified ORB (Oriented FAST and Rotated BRIEF) features represent the landmarks for designing a grid feature detection algorithm. An upgraded feature matching method has improved the robustness of feature matching. The Modified coVariance Extended Kalman Filter (MVEKF) estimates the multiple dimension states of the free moving visual sensor instead of the familiar Extended Kalman Filter (EKF). The simulation navigation of Lunar and Mars surfaces proves that the proposed method is robust and efficient.",

keywords = "extended kalman filter, motion estimation, ORB, planetary rover, simultaneous localisation and mapping, System design",

author = "Tseng, \{Kuo Kun\} and Jun Li and Yachin Chang and Yung, \{K. L.\} and Chan, \{C. Y.\} and Hsu, \{Chih Yu\}",

note = "Funding Information: This work was supported by the Shenzhen Government [Grant[JCYJ20160531191837793, KQJSCX201707261040333]; Shenzhen Development and Reform Commission through Shenzhen Development and Reform [Grant(2016)889]. This research is also partially supported by a grant from the Department of Industrial and Systems Engineering at the Hong Kong Polytechnic University (project code: H-ZG3K). Publisher Copyright: {\textcopyright} 2021 Informa UK Limited, trading as Taylor \& Francis Group.",

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AU - Chan, C. Y.

AU - Hsu, Chih Yu

N1 - Funding Information: This work was supported by the Shenzhen Government [Grant[JCYJ20160531191837793, KQJSCX201707261040333]; Shenzhen Development and Reform Commission through Shenzhen Development and Reform [Grant(2016)889]. This research is also partially supported by a grant from the Department of Industrial and Systems Engineering at the Hong Kong Polytechnic University (project code: H-ZG3K). Publisher Copyright: © 2021 Informa UK Limited, trading as Taylor & Francis Group.

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AB - A new architecture implements one Monocular Simultaneous Localization and Mapping (SLAM) system to track the unconstraint motion of a mobile robot. The modified ORB (Oriented FAST and Rotated BRIEF) features represent the landmarks for designing a grid feature detection algorithm. An upgraded feature matching method has improved the robustness of feature matching. The Modified coVariance Extended Kalman Filter (MVEKF) estimates the multiple dimension states of the free moving visual sensor instead of the familiar Extended Kalman Filter (EKF). The simulation navigation of Lunar and Mars surfaces proves that the proposed method is robust and efficient.

KW - extended kalman filter

KW - motion estimation

KW - ORB

KW - planetary rover

KW - simultaneous localisation and mapping

KW - System design

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A new architecture for simultaneous localization and mapping: an application of a planetary rover

Abstract

Keywords

ASJC Scopus subject areas

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