A tracking system that will be used for augmented reality applications has two main requirements: accuracy and frame rate. The first requirement is related to the performance of the pose estimation algorithm and how accurately the tracking system can find the position and orientation of the user in the environment. Accuracy problems of current tracking devices, considering that they are low-cost devices, cause static errors during this motion estimation process. The second requirement is related to dynamic errors (the end-to-end system delay, occurring because of the delay in estimating the motion of the user and displaying images based on this estimate). This paper investigates combining the vision-based estimates with measurements from other sensors, GPS and IMU, in order to improve the tracking accuracy in outdoor environments. The idea of using Fuzzy Adaptive Multiple Models was investigated using a novel fuzzy rule-based approach to decide on the model that results in improved accuracy and faster convergence for the fusion filter. Results show that the developed tracking system is more accurate than a conventional GPS–IMU fusion approach due to additional estimates from a camera and fuzzy motion models. The paper also presents an application in cultural heritage context running at modest frame rates due to the design of the fusion algorithm.
Recently, Oskiper @cite_12 developed a tightly-coupled EKF visual--inertial tracking system for AR for outdoor environments using a relatively expensive sensor (XSens, MTi-G). The system used feature-level tracking in each frame and measurements from the GPS in order to reduce drift. In addition to this, a digital elevation map of the environment was used as well as a pre-built landmark database for tracking in indoor environments where GPS reception is not available (although it was claimed that no assumption about the environment was made). The error was found to be @math metres.
@cite_12
1996213997
Camera tracking system for augmented reality applications that can operate both indoors and outdoors is described. The system uses a monocular camera, a MEMS-type inertial measurement unit (IMU) with 3-axis gyroscopes and accelerometers, and GPS unit to accurately and robustly track the camera motion in 6 degrees of freedom (with correct scale) in arbitrary indoor or outdoor scenes. IMU and camera fusion is performed in a tightly coupled manner by an error-state extended Kalman filter (EKF) such that each visually tracked feature contributes as an individual measurement as opposed to the more traditional approaches where camera pose estimates are first extracted by means of feature tracking and then used as measurement updates in a filter framework. Robustness in feature tracking and hence in visual measurement generation is achieved by IMU aided feature matching and a two-point relative pose estimation method, to remove outliers from the raw feature point matches. Landmark matching to contain long-term drift in orientation via on the fly user generated geo-tiepoint mechanism is described.