Robust and real-time pose tracking for augmented reality on mobile devices

This paper addresses robust and ultrafast pose tracking on mobile devices, such as smartphones and small drones. Existing methods, relying on either vision analysis or inertial sensing, are either too computational heavy to achieve real-time performance on a mobile platform, or not sufficiently robust to address unique challenges in mobile scenarios, including rapid camera motions, long exposure time of mobile cameras, etc. This paper presents a novel hybrid tracking system which utilizes on-device inertial sensors to greatly accelerate the visual feature tracking process and improve its robustness. In particular, our system adaptively resizes each video frame based on inertial sensor data and applies a highly efficient binary feature matching method to track the object pose in each resized frame with little accuracy degradation. This tracking result is revised periodically by a model-based feature tracking method (Hare et al. 2012) to reduce accumulated errors. Furthermore, an inertial tracking method and a solution of fusing its results with the feature tracking results are employed to further improve the robustness and efficiency. We first evaluate our hybrid system using a dataset consisting of 16 video clips with synchronized inertial sensing data and then assess its performance in a mobile augmented reality application. Experimental results demonstrated our method's superior performance to a state-of-the-art feature tracking method (Hare et al. 2012), a direct tracking method (Engel et al. 2014) and the Vuforia SDK (Ibaez and Figueras 2013), and can run at more than 40 Hz on a standard smartphone. We will release the source code with the pubilication of this paper.