Different approaches for the estimation of the states of linear dynamic systems are commonly used, the most common being the Kalman filter. For nonlinear systems, variants of the Kalman filter are used. Some of these variants include the LKF (linearized Kalman filter), the EKF (extended Kalman filter), and the UKF (unscented Kalman filter). With the LKF and EKF, performance varies depending on ...

The most frequently used method in a three dimensional human gait analysis involves placing markers on the skin of the analyzed segment. This introduces a significant artifact, which strongly influences the bone position and orientation and joint kinematic estimates. In this study, we tested and evaluated the effect of adding a Kalman filter procedure to the previously reported point cluster te...

This paper provides a gentle introduction to the Kalman filter, a numerical method that can be used for sensor fusion or for calculation of trajectories. First, we consider the Kalman filter for a one-dimensional system. The main idea is that the Kalman filter is simply a linear weighted average of two sensor values. Then, we show that the general case has a similar structure and that the mathe...

Control of a class of uncertain nonlinear systems, which estimates unavailable state variables, is considered. A new approach for robust tracking control problem of satellite for large rotational maneuvers is presented in this paper. The features of this approach include a strong algorithm to estimate attitude, based on discrete extended Kalman filter combined with a continuous extended Kalman ...

Pose estimation is a critical skill in mobile robotics and is often accomplished using onboard sensors and a Kalman filter estimation technique. For systems to run online, computational efficiency of the filter design is crucial, especially when faced with limited computing resources. In this paper, we present a novel approach to serially process high-dimensional measurements in the Sigma-Point...

In this paper, a homogenous multi-sensor fusion method is used to estimate the trueangular rate and acceleration with a combination of four low cost (< 10$) MEMS Inertial MeasurementUnits (IMU). An information form of steady state Kalman filter is designed to fuse the output of four lowaccuracy sensors to reduce the noise effect by the square root of the number of sensors. A hardware isimplemen...

in this study, the problem of navigation in dynamic and unknown environment is investigated and a navigation method based on force field approach is suggested. it is assumed that the robot performs navigation in unknown environment and builds the map through slam procedure. since the moving objects&apos; location and properties are unknown, they are identified and tracked by kalman filter. kalm...

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...

In this paper, a six-dimensional (6D) Simultaneous Localization and Mapping (SLAM) based on novel Linear Regression Kalman Filter (LRKF), called Smart Sampling Kalman Filter (S2KF), is proposed. While the conventional feature based SLAM methods use point features as landmarks, only a few take the advantage of geometric information like corners, edges, and planes. A feature based SLAM method usi...