Volume IV-2/W4
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 371-376, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W4-371-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 371-376, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W4-371-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  14 Sep 2017

14 Sep 2017

INDOOR POSITIONING BY VISUAL-INERTIAL ODOMETRY

M. Ramezani, D. Acharya, F. Gu, and K. Khoshelham M. Ramezani et al.
  • Department of Infrastructure Engineering, The University of Melbourne, Parkville 3010 Australia

Keywords: Visual-Inertial Odometry, Kalman Filter, Omnidirectional Cameras, Inertial Measurements, Indoor Positioning

Abstract. Indoor positioning is a fundamental requirement of many indoor location-based services and applications. In this paper, we explore the potential of low-cost and widely available visual and inertial sensors for indoor positioning. We describe the Visual-Inertial Odometry (VIO) approach and propose a measurement model for omnidirectional visual-inertial odometry (OVIO). The results of experiments in two simulated indoor environments show that the OVIO approach outperforms VIO and achieves a positioning accuracy of 1.1 % of the trajectory length.