Past and state-of-the-art SLAM technologies

Jae-Bok Song, Seo Yeon Hwang

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

This paper surveys past and state-of-the-art SLAM technologies. The standard methods for solving the SLAM problem are the Kalman filter, particle filter, graph, and bundle adjustment-based methods. Kalman filters such as EKF (Extended Kalman Filter) and UKF (Unscented Kalman Filter) have provided successful results for estimating the state of nonlinear systems and integrating various sensor information. However, traditional EKF-based methods suffer from the increase of computation burden as the number of features increases. To cope with this problem, particle filter-based SLAM approaches such as FastSLAM have been widely used. While particle filter-based methods can deal with a large number of features, the computation time still increases as the map grows. Graph-based SLAM methods have recently received considerable attention, and they can provide successful real-time SLAM results in large urban environments.

Original languageEnglish
Pages (from-to)372-379
Number of pages8
JournalJournal of Institute of Control, Robotics and Systems
Volume20
Issue number3
DOIs
Publication statusPublished - 2014 Jan 1

Fingerprint

Simultaneous Localization and Mapping
Kalman filters
Kalman Filter
Extended Kalman filters
Particle Filter
Nonlinear systems
Graph in graph theory
Sensors
Bundle
Adjustment
Nonlinear Systems
Real-time
Sensor

Keywords

  • Bundle adjustment
  • GraphSLAM
  • Kalman filter
  • Mobile robot
  • Particle filter
  • SLAM

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Applied Mathematics

Cite this

Past and state-of-the-art SLAM technologies. / Song, Jae-Bok; Hwang, Seo Yeon.

In: Journal of Institute of Control, Robotics and Systems, Vol. 20, No. 3, 01.01.2014, p. 372-379.

Research output: Contribution to journalArticle

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