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 publicationJournal articleAcademic researchpeer-review

1 Citation (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 languageEnglish
Pages (from-to)1162-1178
Number of pages17
JournalEnterprise Information Systems
Volume15
Issue number8
DOIs
Publication statusPublished - 2021

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

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