A practical 2D/3D SLAM using directional patterns of an indoor structure

Keonyong Lee, Soo Hyun Ryu, Changjoo Nam, Nakju Doh

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

This paper presents a practical two-dimensional (2D)/three-dimensional (3D) simultaneous localization and mapping (SLAM) algorithm using directional features for ordinary indoor environments; this algorithm is adaptable to various conditions, computationally inexpensive, and accurate enough to use for practical applications. The proposed algorithm uses odometry acquired from other sensors or other algorithms as the initial estimate and the directional features of indoor structures as landmarks. The directional features can only correct the rotation error of the odometry. However, we show that the greater part of the translation error of the odometry can also be corrected when the directional features are detected at almost positions accurately. In that case, there is no need to use other kinds of features to correct translation error. The directions of indoor structures have two advantages as landmarks. First, the extraction of them is not affected by obstacles. Second, the number of them is small regardless of the size of the building. Because of these advantages, the proposed SLAM algorithm shows robustness for parameters and lightweight properties. From extensive experiments with 2D/3D datasets taken from different buildings, we show the practicality of the proposed algorithm. We also demonstrate that the 2D algorithm runs in real time on a low-end smartphone.

Original languageEnglish
Pages (from-to)1-24
Number of pages24
JournalIntelligent Service Robotics
DOIs
Publication statusAccepted/In press - 2017 Aug 17

Keywords

  • Directional feature
  • Indoor environments
  • Kalman filters
  • Lightweight algorithm
  • Practical algorithm
  • Simultaneous localization and mapping (SLAM)

ASJC Scopus subject areas

  • Computational Mechanics
  • Engineering (miscellaneous)
  • Mechanical Engineering
  • Artificial Intelligence

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