Indoor 3D position estimation using low-cost inertial sensors and marker-based video-tracking

In this paper, a system for indoor 3D position tracking with an inertial measurement unit and a marker-based video tracking system utilizing external cameras is presented. Similar to an integrated navigation system, 3D position, velocity and attitude are calculated from IMU measurements and aided by using position corrections from the video tracking system. The measurements from both sensor sources are fused with an extended Kalman filter model, which incorporates the estimation of IMU biases for drift compensation during video outages. The performance of the filter approach has been tested with simulated data and the whole system has been evaluated with real data from a hand tracking scenario. By means of the combination of inertial sensors and vision-based position tracking, the proposed system is able to overcome video measurement outages over short periods of time as well as drift problems of the IMU.