iTracker: Towards Sustained Self-Tracking in Dynamic Feature Environment with Smartphones
2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)(2019)
摘要
Self-tracking at 6 degrees of freedom in real-time is essential in lots of emerging applications such as VR/AR/MR simulation, indoor navigation, and so on. With the development of built-in sensors in smartphones, many self-tracking solutions have appeared. Many researchers try to utilize vision-based approaches combined with an Inertial Measure Unit (IMU) to realize self-tracking with smartphones. After testing these approaches, however, we find that tracking would be lost in four such common scenarios: 1) When the IMU rotates fast or for a long period of time, it will cause serious delays in orientation tracking; 2) The scenes where background features are not distinct enough; 3) When the smartphone moves fast, image features become quite different in successive frames; 4) Unstructured scenes where background features are not static. To address these issues, we propose iTracker, which utilizes Real-time Step-Length Adaption Algorithm to solve the scenario (1) and a Parallel-Multi-State Local Recovery method to deal with scenarios (2)-(4). Extensive experiments show that iTracker realizes robust and accurate self-tracking in these four scenarios with an error of 0.7% throughout the whole trajectory.
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关键词
iTracker,dynamic feature environment,smartphone,indoor navigation,vision-based approaches,Inertial Measure Unit,IMU,orientation tracking,background features,image features,Real-time Step-Length Adaption Algorithm,sustained self-tracking,parallel-multistate local recovery method
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