The Kinesthetic Node and Autonomous Table-Top Emulator (KNATTE) is a three-degree-of-freedom frictionlessvehicle that serves as a multipurpose platform for real-time spacecraft hardware-in-the-loop experiments. The dataacquisition of the vehicle depends on a Computer Vision System (CVS) that yields position and attitude data, but alsosuffers from unpredictable blackout events. To complement such measurements, KNATTE incorporates an InertialMeasurement Unit (IMU) that yields accelerometer, gyroscope, and magnetometer data. This study describes amultisensor data fusion approach to obtain accurate attitude information by combining the measurements from theCVS and the IMU using nonlinear Kalman filter algorithms. To do this, we develop the data fusion algorithms andtest them in a MATLAB/Simulink environment. After that, we adapt the algorithms to the KNATTE platform andconfirm the performance in various conditions. Through this work, we can check the accuracy and efficiency of theapproach by numerical simulation and real-time experiments.