A high-performance tracking system based on camera and IMU

We consider an indoor tracking system consisting of an inertial measurement unit (IMU) and a camera that detects markers in the environment. There are many camera based tracking systems described in literature and available commercially, and a few of them also has support from IMU. These are based on the best-effort principle, where the performance varies depending on the situation. In contrast to this, we start with a specification of the system performance, and the design is based on an information theoretic approach, where specific user scenarios are defined. Precise models for the camera and IMU are derived for a fusion filter, and the theoretical Cramér-Rao lower bound and the Kalman filter performance are evaluated. In this study, we focus on examining the camera quality versus the marker density needed to get at least a one mm and one degree accuracy in tracking performance.