Sensor fusion in head pose tracking for augmented reality

The focus of this thesis is on studying diverse techniques, methods and sensors for position and orientation determination with application to augmented reality applications. In Chapter 2 we reviewed a variety of existing techniques and systems for position determination. From a practical point of view, we discussed the need for a mobile system to localize itself while navigating through an environment. We identified two different localization instantiations, position tracking and global localization. In order to determine what information a mobile system has access to regarding its position, we discussed different sources of information and pointed out advantages and disadvantages. We concluded that due to the imperfections in actuators and sensors due to noise sources, a navigating mobile system should localize itself using information from different sensors. In Chapter 3, based on the analysis of the technologies presented in the Chapter 2 and the sensors described in this chapter, we selected a set of sensors from which to acquire and fuse the data in order to achieve the required robustness and accuracy. We selected for the inertial system three accelerometers (ADXL105) and three gyroscopes (Murata ENC05). To correct for gyro drift we use a TCM2 sensor that contains a two-axis inclinometer and a three-axes magnetometer (compass). Indoors we use a Firewire webcam to obtain the position and orientation information. Outdoors we use, in addition, a GPS receiver in combination with a radio data system (RDS) receiver to obtain DGPS correction information. Chapter 4 was concerned with development of inertial equations required for the navigation of a mobile system. To understand the effect of error propagation, the inertial equations were linearized. In this chapter we decompose the localization problem into attitude estimation and, subsequently, position estimation. We focus on obtaining a good attitude estimate without building a model of the vehicle dynamics. The dynamic model was replaced by gyro modeling. An Indirect (error state) Kalman filter that optimally incorporates inertial navigation and absolute measurements was developed for this purpose. The linear form of the system and measurement equations for the planar case derived here allowed us to examine the role of the Kalman filter as a signal processing unit. The extension of this formulation to the 3D case shows the same benefits. A tracking example in the 3D case was also shown in this chapter. Chapter 5 details all the necessary steps for implementing a vision positioning system. The pose tracking system for outdoor augmented reality is partly based on a vision system that tracks the head position within centimeters, the head orientation within degrees, and has an update rate of within a second. The algorithms that are necessary to obtain a robust vision system for tracking the automotion of a camera based on its observations of the physical world contain feature detection algorithms, camera calibration routines and pose determination algorithms. In Chapter 6 we summarize the presented work with concluding remarks. Here, we also present ideas and possibilities for future research. The conclusion is that since existing technology or sensor alone cannot solve the pose problem, we combine information from multiple sensors to obtain a more accurate and stable system. We present the development of an entire position determination system using off-the-shelve existing sensors integrated using separate Kalman filters. A unified solution is presented: inertial measurement integration for orientation and GPS in combination with a differential correction unit for positioning. PDF (corr1) uploaded: 28-5-2014