ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 371-376, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
14 Sep 2017
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,, 2017.

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