scholarly journals An O(N²) Square Root Unscented Kalman Filter for Visual Simultaneous Localization and Mapping

2009 ◽  
Vol 31 (7) ◽  
pp. 1251-1263 ◽  
Author(s):  
S.A. Holmes ◽  
G. Klein ◽  
D.W. Murray
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Simultaneous Localization And Mapping ◽  
Square Root ◽  
Localization And Mapping

Intelligent FastSLAM: An Intelligent Factorized Solution to Simultaneous Localization and Mapping

2017 ◽  
Vol 14 (01) ◽  
pp. 1650026 ◽  
Author(s):  
Ramazan Havangi
Keyword(s):  
Kalman Filter ◽  
Pose Estimation ◽  
Fuzzy Inference ◽  
Unscented Kalman Filter ◽  
Simultaneous Localization And Mapping ◽  
Inference System ◽  
Localization And Mapping ◽  
Position And Orientation ◽  
Parametric Filter ◽  
Over Time

FastSLAM is a well-known solution to the simultaneous localization and mapping (SLAM) problem. In FastSLAM, a nonparametric filter is used for the mobile robot pose (position and orientation) estimation, and a parametric filter is used for the feature location's estimation. The performance of the conventional FastSLAM degrades over time due to the particle depletion and unknown statistic noises. In this paper, intelligent FastSLAM (IFastSLAM) is proposed. In this approach, an evolutionary filter (EF) searches stochastically along with the state space for the best robot's pose estimation and an adaptive fuzzy unscented Kalman filter (AFUKF) is used for the feature location's estimation. In AFUKF, a fuzzy inference system (FIS) supervises the performance of the unscented Kalman filter with the aim of reducing the mismatch between the theoretical and actual covariance of the innovation sequences in order to get better consistency. We demonstrate the proposed algorithm with simulations and real-world experiments. The results show that the proposed method is effective, and its performance outperforms conventional FastSLAM.

scholarly journals AN APPROACH ON ADVANCED UNSCENTED KALMAN FILTER FROM MOBILE ROBOT-SLAM

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 381-389
Author(s):  
L. Yan ◽  
L. Zhao
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Jacobian Matrix ◽  
Unscented Kalman Filter ◽  
Classical Method ◽  
Nonlinear Function ◽  
Root Decomposition ◽  
Square Root ◽  
Online Processing ◽  
Localization And Mapping

Abstract. In the past 30 years, Kalman filter is a classical method to solve the problem of simultaneous localization and mapping (SLAM) of mobile robots. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) are derived from Kalman filter. Extended Kalman filter (EKF) overcomes the nonlinear problem that Kalman filter cannot solve. However, when it is strongly nonlinear, EKF violates the assumption of local linearity, and EKF algorithm may make the filter diverge. Secondly, because the Jacobian matrix is needed in the online processing of EKF, its tedious calculation process makes the implementation of this method relatively difficult. Unscented Kalman filter (UKF) uses nonlinear model directly, avoids operation of Jacobian matrix of complex nonlinear function, and ensures the general adaptability of nonlinear system. In this paper, based on the square root unscented Kalman filter, sigma points are selected according to the square-root decomposition of prior covariance, and then weighted mean and covariance are calculated. The quaternion is used to represent the attitude, and the quaternion vector is converted to the rotation space for matrix operation. Comparing the robot poses estimated based on the square root traceless Kalman filter (QSR-UKF), square root traceless Kalman filter (SR-UKF), and extended Kalman filter (EKF), the simulation results show the QSR-UKF proposed in this paper is effective.

scholarly journals A FastSLAM Algorithm Based on Nonlinear Adaptive Square Root Unscented Kalman Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yu-feng Zhang ◽  
Qi-xun Zhou ◽  
Ju-zhong Zhang ◽  
Yi Jiang ◽  
Kai Wang
Keyword(s):  
Kalman Filter ◽  
Indoor Environment ◽  
Unscented Kalman Filter ◽  
Square Root ◽  
Angle Error ◽  
3 Dimensional ◽  
Localization And Mapping ◽  
Traditional Algorithm ◽  
Model Linearization ◽  
Posteriori Estimation

For fast simultaneous localization and mapping (FastSLAM) problem, to solve the problems of particle degradation, the error introduced by linearization and inconsistency of traditional algorithm, an improved algorithm is described in the paper. In order to improve the accuracy and reliability of algorithm which is applied in the system with lower measurement frequency, a new decomposition strategy is adopted for a posteriori estimation. In proposed decomposition strategy, the problem of solving a 3-dimensional state vector and N 2-dimensional state vectors in traditional FastSLAM algorithm is transformed to the problem of solving N 5-dimensional state vectors. Furthermore, a nonlinear adaptive square root unscented Kalman filter (NASRUKF) is used to replace the particle filter and Kalman filter employed by traditional algorithm to reduce the model linearization error and avoid solving Jacobian matrices. Finally, the proposed algorithm is experimentally verified by vehicle in indoor environment. The results prove that the positioning accuracy of proposed FastSLAM algorithm is less than 1 cm and the azimuth angle error is 0.5 degrees.

scholarly journals A square root unscented Kalman filter for multiple view geometry based stereo cameras/inertial navigation

2016 ◽  
Vol 13 (5) ◽  
pp. 172988141666485 ◽  
Author(s):  
Zhiwen Xian ◽  
Junxiang Lian ◽  
Mao Shan ◽  
Lilian Zhang ◽  
Xiaofeng He ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Inertial Navigation ◽  
Square Root ◽  
Multiple View Geometry ◽  
Multiple View ◽  
Stereo Cameras

Model aided airborne integrated navigation system based on an improved square-root unscented H∞ filter

2018 ◽  
Vol 41 (5) ◽  
pp. 1290-1300
Author(s):  
Jieliang Shen ◽  
Yan Su ◽  
Qing Liang ◽  
Xinhua Zhu
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Unscented Kalman Filter ◽  
Statistical Characteristics ◽  
Small Scale ◽  
Strong Nonlinearity ◽  
Square Root ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Integrated Navigation System

An inertial navigation system (INS) aided with an aircraft dynamic model (ADM) is developed as a novel airborne integrated navigation system, coping with the absence of a global navigation satellite system. To overcome the shortcomings of the conventional linear integration of INS/ADM based on an extended Kalman filter, a nonlinear integration method is proposed. Fast-update ADM makes it possible to utilize a direct filtering method, which employs nonlinear INS mechanics as system equations and a nonlinear ADM as observation equations, substituting the indirect filtering based on linear error equations. The strong nonlinearity generally calls for an unscented Kalman filter to accomplish the fusion process. Dealing with the model uncertainty, the inaccurate statistical characteristics of the noise and the potential nonpositive definiteness of the covariance matrix, an improved square-root unscented H∞ filter (ISRUHF) is derived in the paper, in which the robust factor [Formula: see text] is further expanded into a diagonal matrix [Formula: see text], to improve the accuracy and robustness of the integrated navigation system. Corresponding simulations as well as real flight tests based on a small-scale fixed-wing aircraft are operated and ISRUHF shows superiority compared with the commonly used fusion algorithm.

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