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...

In this paper, we study estimator inconsistency in Vision-aided Inertial Navigation Systems (VINS). We show that standard (linearized) estimation approaches, such as the Extended Kalman Filter (EKF), can fundamentally alter the system observability properties, in terms of the number and structure of the unobservable directions. This in turn allows the influx of spurious information, leading to ...

We introduce a new mathematical method for tracking position and orientation. The method, which is applicable to a wide variety of virtual-environment tracking systems, offers several advantages: (1) significantly improved estimate rates and latencies; (2) improved accuracy; (3) a flexible framework for heterogeneous multi-sensor data fusion; and (4) a unique opportunity to perform simultaneous...

A biochemical dynamic pathway is usually modeled as a nonlinear system described by a set of nonlinear ODEs. In most cases, only partial states can be measured. Moreover, the system parameters, reaction rates, may be unknown or poorly known. Therefore, it is of significance to estimate the states and parameters, for analyzing the biochemical dynamic pathway. Due to the limitation of some tradit...

The classical approach to designing lters for systems where system equations are linear and measurement equations are nonlinear is to linearise measurement equations, and apply an Extended Kalman Filter (EKF). This results in suboptimal, biased, and often divergent lters. Many schemes proposed to improve the performance of the EKF concentrated on better linearisation techniques, iterative techn...

We present a new motion estimation framework and apply it to omni-directional imagery. Our method estimates motions incrementally using an Implicit Extended Kalman Filter (IEKF). Each individual feature provides partial information about the camera motion. The motion estimate is incrementally improved as each feature is processed, similar to the SCAAT approach. The SCAAT method was developed fo...