The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper addresses the estimation problem for linear system with linear equality state constraints. We review the existing state projection method and present a simple way to find the optimal filter gain of the constrained Kalman filter. Then, we transform the constrained system into a reduced-order model and construct a reduced-order Kalman filter for this estimation problem. Next, we discuss the...
In this paper, we address a method for improving the accuracy of the feature map from the extended Kalman filter based SLAM (EKF SLAM) by estimating the systematic parameters of the robot. Most error of the robot while traveling is divided into two categories: systematic and non systematic error. The systematic error contributes much more to odometry errors than non systematic one on most smooth indoor...
In this paper, we address a method for improving accuracy of a Neural Network (NN) aided Extended Kalman Filter (EKF) based SLAM by compensating for an odometric error of a robot. The NN is used for estimating the odometric error and online learning of NN is implemented by augmenting the synaptic weights of the NN as the elements of state vector in the EKF-SLAM process. Due to this trainability, the...
The recursive filtering of discrete-time nonlinear systems in the presence of unknown noise statistical parameters is studied. By embedding the modified Sage-Husa noise statistics estimator into the iterated Kalman filter, an adaptive iterated Kalman filter is obtained. With iterative operations as well as the online estimation of unknown covariance of virtual noise, linearized error can be reduced...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.