We present a novel technique for the automatic adaptation of a deformable model’s elastic parameters within a Kalman filter framework for shape estimation applications. The novelty of the technique is that the model’s elastic parameters are not constant, but spatio-temporally varying. The variation of the elastic parameters depends on the distance of the model from the data and the rate of chan...

Abstract: Driving safety can be enhanced by better understanding of risk situation, which can be achieved by the knowledge of vehicle dynamic states as well as the road geometry. Among the parameters of the road that have an influence on vehicle dynamics, one can find the bank angle, which can not however be measured by low cost onboard sensors. In this paper, a new method of road bank angle an...

In this paper, the design and real time implementation of a Nonlinear Minimum Variance (NMV) estimator is presented using a laboratory based ball and beam system. The real time implementation employs a LabVIEW based tool. The novelty of this work lies in the design steps and the practical implementation of the NMV estimation technique which up till now only investigated using simulation studies...

We present a feature-based probabilistic map building algorithm which directly utilizes time and amplitude information of sonar in indoor environments. Utilizing additional amplitude-of-signal (AOS) obtained concurrently with time-of-flight (TOF), the amount of inclination of target can be directly calculated from a single echo, and the number of measurements can be greatly reduced with result ...

In this paper we compare multi hypothesis localisation (MHL)—which is a mobile robot localisation method based on multi hypothesis tracking—with six other methods reported in the literature. The comparison is performed using a standard set of test data and corresponding evaluation tools, thus facilitating a direct comparison of the obtained results. The experiments show that MHL compares favour...

In this paper we present an multi-robotic system for underwater exploration, specifically for coastal seas. The novelty of this system is its enhanced performance in underwater localization using underwater acoustic ranging and data transfer between the floats. This process is not dependent on any fixed infrastructure, which is usually a requirement for such missions. An algorithm is implemente...

The Kalman lter(KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application of the KF to nonlinear systems can be diicult. The most common approach is to use the Extended Kalman Filter (EKF) which simply linearises all nonlinear models so that the traditional linear Kalman lter can be applied. Al...

A system that builds and maintains a dynamic map for a mobile robot is presented. A learning rule associated to each observed landmark is used to compute its robustness. The position of the robot during map construction is estimated by combining sensor readings, motion commands, and the current map state by means of an Extended Kalman Filter. The combination of landmark strength validation and ...

For high performance speed and position control of electrical drives fast online identification is needed for time-varying inertia or load conditions in combination with adaptive controllers. In this paper Extended Kalman Filters are applied and optimized for deterministic parameter variations by integrating basis function networks into the common structure of the Kalman Filter. It is shown tha...