KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2013 · 12 citations · 0 references
Open access
ComputingComputer ScienceRobotics EngineeringComputer VisionMobile RoboticsSensor FusionIndoor LocalizationKalman FilteringMotion CapturePose EstimationTracking SystemsVisual TrackingAccuracyInertial Measurement Unit FusionInformation-theoretic FusionKalman Filter FusionKalman Filter TrackingMulti-sensor Kalman FusionSystem Performance ModelingUser Scenarios
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 isbased 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.