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...

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...

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...