Simultaneous localization and mapping with neuro-fuzzy assisted extended Kalman filtering

2017 ◽  
Author(s):  
Cong Hung Do ◽  
Huei-Yung Lin ◽  
Yi-Chun Huang
Keyword(s):  
Kalman Filtering ◽  
Simultaneous Localization And Mapping ◽  
Extended Kalman Filtering ◽  
Localization And Mapping ◽  
Neuro Fuzzy

Invariant Kalman Filtering

2018 ◽  
Vol 1 (1) ◽  
pp. 237-257 ◽  
Author(s):  
Axel Barrau ◽  
Silvère Bonnabel
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Autonomous Vehicle ◽  
Autonomous Systems ◽  
Practical Application ◽  
Extended Kalman Filtering ◽  
Industrial Implementation ◽  
Localization And Mapping ◽  
And Robotics ◽  
Insight Into

The Kalman filter—or, more precisely, the extended Kalman filter (EKF)—is a fundamental engineering tool that is pervasively used in control and robotics and for various estimation tasks in autonomous systems. The recently developed field of invariant extended Kalman filtering uses the geometric structure of the state space and the dynamics to improve the EKF, notably in terms of mathematical guarantees. The methodology essentially applies in the fields of localization, navigation, and simultaneous localization and mapping (SLAM). Although it was created only recently, its remarkable robustness properties have already motivated a real industrial implementation in the aerospace field. This review aims to provide an accessible introduction to the methodology of invariant Kalman filtering and to allow readers to gain insight into the relevance of the method as well as its important differences with the conventional EKF. This should be of interest to readers intrigued by the practical application of mathematical theories and those interested in finding robust, simple-to-implement filters for localization, navigation, and SLAM, notably for autonomous vehicle guidance.

scholarly journals Incorporating neuro-fuzzy with extended Kalman filter for simultaneous localization and mapping

2019 ◽  
Vol 16 (5) ◽  
pp. 172988141987464 ◽  
Author(s):  
Cong Hung Do ◽  
Huei-Yung Lin
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Extended Kalman Filter ◽  
Optimization Technique ◽  
Computational Cost ◽  
Simultaneous Localization And Mapping ◽  
Mobile Robot Navigation ◽  
Filter Technique ◽  
Localization And Mapping ◽  
Neuro Fuzzy

Extended Kalman filter is well-known as a popular solution to the simultaneous localization and mapping problem for mobile robot platforms or vehicles. In this article, the development of a neuro-fuzzy-based adaptive extended Kalman filter technique is presented. The objective is to estimate the proper values of the R matrix at each step. We design an adaptive neuro-fuzzy extended Kalman filter to minimize the difference between the actual and theoretical covariance matrices of the innovation consequence. The parameters of the adaptive neuro-fuzzy extended Kalman filter is then trained offline using a particle swarm optimization technique. With this approach, the advantages of high-dimensional search space can be exploited and more effective training can be achieved. In the experiments, the mobile robot navigation with a number of landmarks under two benchmark situations is evaluated. The results have demonstrated that the proposed adaptive neuro-fuzzy extended Kalman filter technique provides the improvement in both performance efficiency and computational cost.

scholarly journals Novel indoor positioning algorithm based on Lidar/inertial measurement unit integrated system

2021 ◽  
Vol 18 (2) ◽  
pp. 172988142199992
Author(s):  
Ping Jiang ◽  
Liang Chen ◽  
Hang Guo ◽  
Min Yu ◽  
Jian Xiong
Keyword(s):  
Mobile Robot ◽  
Kalman Filtering ◽  
Inertial Measurement Unit ◽  
Simultaneous Localization And Mapping ◽  
Measurement Unit ◽  
Accuracy Rate ◽  
Inertial Measurement ◽  
Mean Error ◽  
Localization And Mapping ◽  
The Mean

As an important research field of mobile robot, simultaneous localization and mapping technology is the core technology to realize intelligent autonomous mobile robot. Aiming at the problems of low positioning accuracy of Lidar (light detection and ranging) simultaneous localization and mapping with nonlinear and non-Gaussian noise characteristics, this article presents a mobile robot simultaneous localization and mapping method that combines Lidar and inertial measurement unit to set up a multi-sensor integrated system and uses a rank Kalman filtering to estimate the robot motion trajectory through inertial measurement unit and Lidar observations. Rank Kalman filtering is similar to the Gaussian deterministic point sampling filtering algorithm in structure, but it does not need to meet the assumptions of Gaussian distribution. It completely calculates the sampling points and the sampling points weights based on the correlation principle of rank statistics. It is suitable for nonlinear and non-Gaussian systems. With multiple experimental tests of small-scale arc trajectories, we can see that compared with the alone Lidar simultaneous localization and mapping algorithm, the new algorithm reduces the mean error of the indoor mobile robot in the X direction from 0.0928 m to 0.0451 m, with an improved accuracy rate of 46.39%, and the mean error in the Y direction from 0.0772 m to 0.0405 m, which improves the accuracy rate of 48.40%. Compared with the extended Kalman filter fusion algorithm, the new algorithm reduces the mean error of the indoor mobile robot in the X direction from 0.0597 m to 0.0451 m, with an improved accuracy rate of 24.46%, and the mean error in the Y direction from 0.0537 m to 0.0405 m, which improves the accuracy rate of 24.58%. Finally, we also tested on a large-scale rectangular trajectory, compared with the extended Kalman filter algorithm, rank Kalman filtering improves the accuracy of 23.84% and 25.26% in the X and Y directions, respectively, it is verified that the accuracy of the algorithm proposed in this article has been improved.

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