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Intelligent Service Robotics

, Volume 11, Issue 1, pp 1–24 | Cite as

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

  • Keonyong Lee
  • Soo-Hyun Ryu
  • Changjoo Nam
  • Nakju Lett DohEmail author
Original Research Paper

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.

Keywords

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

Notes

Acknowledgements

This paper is supported by three Korean government funds of 10073166, 2012M3A6A3055700, and 2014K1A3A1A19067398.

Supplementary material

11370_2017_234_MOESM1_ESM.mp4 (139.3 mb)
Supplementary material 1 (mp4 142653 KB)

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Keonyong Lee
    • 1
  • Soo-Hyun Ryu
    • 1
  • Changjoo Nam
    • 2
  • Nakju Lett Doh
    • 1
    • 3
    Email author
  1. 1.School of Electrical Engineering at Korea UniversitySeoulKorea
  2. 2.The Robotics Institute at Carnegie Mellon UniversityPittsburghUSA
  3. 3.CEO, TeeVR Inc.SeoulSouth Korea

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