In this paper, we introduce an Iterative Kalman Smoother (IKS) for tracking the 3D motion of a mobile device in real-time using visual and inertial measurements. In contrast to existing Extended Kalman Filter (EKF)-based approaches, smoothing can better approximate the underlying nonlinear system and measurement models by re-linearizing them. Additionally, by iteratively optimizing over all measurements available, the IKS increases the convergence rate of critical parameters (e.g., IMU-camera clock drift) and improves the positioning accuracy during challenging conditions (e.g., scarcity of visual features). Furthermore, and in contrast to existing inverse filters, the proposed IKS's numerical stability allows for efficient 32-bit implementations on resource-constrained devices, such as cell phones and wearables. We validate the IKS for performing vision-aided inertial navigation on Google Glass, a wearable device with limited sensing and processing, and demonstrate positioning accuracy comparable to that achieved on cell phones. To the best of our knowledge, this work presents the first proof-of-concept real-time 3D indoor localization system on a commercial-grade wearable computer.
|Original language||English (US)|
|Number of pages||8|
|Journal||Proceedings - IEEE International Conference on Robotics and Automation|
|State||Published - Jun 29 2015|
|Event||2015 IEEE International Conference on Robotics and Automation, ICRA 2015 - Seattle, United States|
Duration: May 26 2015 → May 30 2015