State estimators for nonlinear systems are derived based on polynomial approximations obtained with a multi-dimensional interpolation formula. It is shown that under certain assumptions the estimators perform better than estimators based on Taylor approximations. Nevertheless, the implementation is signi"cantly simpler as no derivatives are required. Thus, it is believed that the new state esti...

In this paper extended Kalman filter (EKF) is reviewed for estimating the rotor position/speed of a vector controlled five-phase induction motor drive. The basic configuration of the Kalman filter is studied and the system vectors and matrices are explained. The EKF equations are made from a d-q-axis model of the five-phase induction motor by considering the rotor speed as a state variable. The...

This paper describes a terrain-aided navigation system that employs points of maximum curvature extracted from laser scan data as primary landmarks. A scale space method is used to extract points of maximum curvature from laser range scans of unmodified outdoor environments. This information is then fused with odometric information to provide localization information for an outdoor vehicle. The...

This paper describes the design of a system for estimating the pose of a camera mounted on a vehicle which moves over a flat floor. The system has been designed to work in conjunction with an obstacle avoidance scheme. Measurements are derived from a sequence of images captured by a single camera rigidly mounted on the vehicle and looking at the floor, and combined with measurements of the vehi...

If a sensor requires finite processing and communication time, then the current sensor reading actually corresponds to the states of a vehicle at some point in the past. At the sensor fusion algorithm, it is expected that large errors may accumulate over time if we simply ignore the fact that the sensor measurement is lagged. Over several decades, the Kalman filter based compensation techniques...

The subject of this article is a scheme for distributed outdoor localization of a team of robots and the use of the robot team for outdoor terrain mapping. Localization is accomplished via Extended Kalman Filtering (EKF). In the distributed EKF-based scheme for localization, heterogeneity of the available sensors is exploited in the absence or degradation of absolute sensors aboard the team mem...

An automatic system for creating a virtual head that is compatible with MPEG-4 facial object specification is presented. Color classification and a valley detection filter are performed to find face and Facial Definition Points (FDPs) at the initialization stage. Extracted FDPs are tracked by normalized correlation and their trajectories are fed into an extended Kalman filter (EKF) to recover c...

The focus of study is to discuss a statistical behavior of FIM in each EKF update and determine its potential in providing sufficient information about Robotic Localization and Mapping problem with intermittent measurements. We provide a theoretical analysis result and prove that the FIM can successfully describe both upper and lower bounds for the state covariance matrix whenever measurement d...

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 ...