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.
Attempts to improve the accuracy of the filtering have also been made using adaptive approaches. In some studies, values for the state and measurement covariance matrices were updated based on the innovation @cite_23 and recently fuzzy logic was used for this task @cite_9 @cite_5 . Another approach for fusing accelerometer and gyroscope for attitude estimation is also based on fuzzy rules @cite_10 in order to decide which of the accelerometer or the gyroscope will be given weight for estimation based on observations from these sensors such as whether a mobile robot is rotating or not. A later approach @cite_30 used the error and dynamic motion parameters in order to decide which sensor should have a dominant effect on the estimation.
@cite_30 @cite_9 @cite_23 @cite_5 @cite_10
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Abstract This paper describes the development of a fuzzy logic based closed-loop strapdown attitude reference system (SARS) algorithm, integrated filtering estimator for determining attitude reference, for unmanned aerial vehicles (UAVs) using low-cost solid-state inertial sensors. The SARS for this research consists of three single-axis rate gyros in conjunction with two single-axis accelerometers. For the solution scheme fuzzy modules (rules and reasoning) are utilized for online scheduling of the parameters for the filtering estimator. Implementation using experimental flight test data of SURV-1 Sejong UAV has been performed in order to verify the estimation. The proposed fuzzy logic aided estimation results demonstrate that more accurate performance can be achieved in comparison with conventional fixed parameter filtering estimators. The estimation results were compared with the on-board vertical gyro used as the reference standard or ‘truth model’ for this analysis. In this paper, the application of the fuzzy interacting multiple model unscented Kalman filter (FUZZY-IMMUKF) approach to integrated navigation processing for the maneuvering vehicle is presented. The unscented Kalman filter (UKF) employs a set of sigma points through deterministic sampling, such that a linearization process is not necessary, and therefore the errors caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. The nonlinear filters naturally suffer, to some extent, the same problem as the EKF for which the uncertainty of the process noise and measurement noise will degrade the performance. As a structural adaptation (model switching) mechanism, the interacting multiple model (IMM), which describes a set of switching models, can be utilized for determining the adequate value of process noise covariance. The fuzzy logic adaptive system (FLAS) is employed to determine the lower and upper bounds of the system noise through the fuzzy inference system (FIS). The resulting sensor fusion strategy can efficiently deal with the nonlinear problem for the vehicle navigation. The proposed FUZZY-IMMUKF algorithm shows remarkable improvement in the navigation estimation accuracy as compared to the relatively conventional approaches such as the UKF and IMMUKF. One of the most important tasks in integration of GPS INS is to choose the realistic dynamic model covariance matrix Q and measurement noise covariance matrix R for use in the Kalman filter. The performance of the methods to estimate both of these matrices depends entirely on the minimization of dynamic and measurement update errors that lead the filter to converge. This paper evaluates the performances of adaptive Kalman filter methods with different adaptations. Innovation and residual based adaptive Kalman filters were employed for adapting R and Q. These methods were implemented in a loose GPS INS integration system and tested using real data sets. Their performances have been evaluated and compared. Their limitations in real-life engineering applications are discussed. The necessity of accurate localization in mobile robotics is obvious—if a robot does not know where it is, it cannot navigate accurately and reach goal locations. Robots learn about their environment via sensors. Small robots require small, efficient, and, if they are to be deployed in large numbers, inexpensive sensors. The sensors used by robots to perceive the world are inherently inaccurate, providing noisy, erroneous data, or even no data at all. Combined with estimation error due to imperfect modeling of the robot, there are many obstacles to successfully localizing in the world. Sensor fusion is used to overcome these difficulties—combining the available sensor data to derive a more accurate pose estimation for the robot. A feeling of “ready-fire-aim'' pervades the discipline—filters are chosen on little to no information, and new filters are simply tested against a few peers and claimed as superior to all others. This is folly—the most appropriate filter is seldom the newest. This article provides an overview and in-depth tutorial of all modern robot localization methods and thoroughly discusses their strengths and weaknesses to assist a robot researcher in the task of choosing the most appropriate filter for their task. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc. Most mobile robots use a combination of absolute and relative sensing techniques for position estimation. Relative positioning techniques are generally known as dead-reckoning. Many systems use odometry as their only dead-reckoning means. However, fiber optic gyroscopes have become more affordable and are being used on many platforms to supplement odometry, especially in indoor applications. Still, if the terrain is not level (i.e., rugged or rolling terrain), the tilt of the vehicle introduces errors into the conversion of gyro readings to vehicle heading. In order to overcome this problem vehicle tilt must be measured and factored into the heading computation. The paper introduces a new fuzzy logic expert rule-based navigation (FLEXnav) method for fusing data from multiple low- to medium-cost gyroscopes and accelerometers in order to estimate accurately the heading and tilt of a mobile robot. Experimental results of mobile robot runs over rugged terrain are presented, showing the effectiveness of our FLEXnav method.