This work focuses on a brief discussion of new concepts of using smartphone\nsensors for 3D painting in virtual or augmented reality. Motivation of this research\ncomes from the idea of using different types of sensors which exist in\nour smartphones such as accelerometer, gyroscope, magnetometer etc. to\ntrack the position for painting in virtual reality, like Google Tilt Brush, but\ncost effectively. Research studies till date on estimating position and localization\nand tracking have been thoroughly reviewed to find the appropriate algorithm\nwhich will provide accurate result with minimum drift error. Sensor fusion,\nInertial Measurement Unit (IMU), MEMS inertial sensor, Kalman filter\nbased global translational localization systems are studied. It is observed, prevailing\napproaches consist issues such as stability, random bias drift, noisy acceleration\noutput, position estimation error, robustness or accuracy, cost effectiveness\netc. Moreover, issues with motions that do not follow laws of\nphysics, bandwidth, restrictive nature of assumptions, scale optimization for\nlarge space are noticed as well. Advantages of such smartphone sensor based\nposition estimation approaches include, less memory demand, very fast operation,\nmaking them well suited for real time problems and embedded systems.\nBeing independent of the size of the system, they can work effectively for high\ndimensional systems as well. Through study of these approaches it is observed,\nextended Kalman filter gives the highest accuracy with reduced requirement\nof excess hardware during tracking. It renders better and faster result\nwhen used in accelerometer sensor. With the aid of various software, error\naccuracy can be increased further as well.
Loading....