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
INDOOR POSITIONING BY VISUAL-INERTIAL ODOMETRY
M. Ramezani, D. Acharya, F. Gu, and K. Khoshelham 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.
Conference paper (PDF, 1935 KB)


Citation: Ramezani, M., Acharya, D., Gu, F., and Khoshelham, K.: INDOOR POSITIONING BY VISUAL-INERTIAL ODOMETRY, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 371-376, https://doi.org/10.5194/isprs-annals-IV-2-W4-371-2017, 2017.

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