INDOOR POSITIONING BY VISUAL-INERTIAL ODOMETRY
- 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.