Estimating the position and orientation of a mobile robot using neural network framework based on combined square-root cubature Kalman filter and simultaneous localization and mapping

For more than two decades, the issue of simultaneous localization and mapping (SLAM) has gained more attention from researchers and remains an influential topic in robotics. Currently, various algorithms of the mobile robot SLAM have been investigated. However, the probability-based mobile robot SLAM algorithm is often used in the unknown environment. In this paper, the authors proposed two main algorithms of localization. First is the linear Kalman Filter (KF) SLAM, which consists of five phases, such as (a) motionless robot with absolute measurement, (b) moving vehicle with absolute measurement, (c) motionless robot with relative measurement, (d) moving vehicle with relative measurement, and (e) moving vehicle with relative measurement while the robot location is not detected. The second localization algorithm is the SLAM with the Extended Kalman Filter (EKF). Finally, the proposed SLAM algorithms are tested by simulations to be efficient and viable. The simulation results show that the presented SLAM approaches can accurately locate the landmark and mobile robot.

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Square-root unscented Kalman filter based simultaneous localization and mapping

The 2010 IEEE International Conference on Information and Automation ◽

10.1109/icinfa.2010.5512187 ◽

2010 ◽

Cited By ~ 4

Author(s):

Shurong Li ◽

Pengfei Ni

Keyword(s):

Kalman Filter ◽

Unscented Kalman Filter ◽

Simultaneous Localization And Mapping ◽

Square Root ◽

Localization And Mapping

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

International Journal of Humanoid Robotics ◽

10.1142/s0219843616500262 ◽

2017 ◽

Vol 14 (01) ◽

pp. 1650026 ◽

Cited By ~ 2

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.

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

International Journal of Advanced Robotic Systems ◽

10.1177/1729881419874645 ◽

2019 ◽

Vol 16 (5) ◽

pp. 172988141987464 ◽

Cited By ~ 1

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.

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Towards simultaneous localization and mapping tolerant to sensors and software faults: Application to omnidirectional mobile robot

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/0959651820932733 ◽

2020 ◽

pp. 095965182093273

Author(s):

Billel Kellalib ◽

Nouara Achour ◽

Vincent Coelen ◽

Abdelkrim Nemra

Keyword(s):

Kalman Filter ◽

Fault Tolerance ◽

Fault Detection ◽

Mobile Robot ◽

Extended Kalman Filter ◽

Simultaneous Localization And Mapping ◽

Fault Detection And Isolation ◽

Software Faults ◽

Isolation Technique ◽

Localization And Mapping

In the last few years, simultaneous localization and mapping became an important topic of research in the robotics community. This article proposes an approach for autonomous navigation of mobile robots in faulty situations. The main objective is to extend the fault tolerance strategy to simultaneous localization and mapping in presence of sensor faults or software faults in the data fusion process. Fault detection and isolation technique is performed based on duplication–comparison method and structured residuals. The proposed fault tolerance approach is based on the extended Kalman filter for simultaneous localization and mapping when an absolute localization sensor is available. The validation of the proposed approach and the extended Kalman filter for simultaneous localization and mapping algorithm is performed from experiments employing an omnidrive mobile robot, equipped with embedded sensors, namely: wheel encoders, gyroscope, two laser rangefinders and external sensor for the absolute position (indoor global positioning system). The obtained results demonstrate the effectiveness of the proposed approach where it was found that its fault tolerance performance is based essentially on the selected residuals and the values of the fault detection thresholds to be used for the fault detection and isolation.

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Square-root Cubature FastSLAM algorithm for mobile robot simultaneous localization and mapping

2012 IEEE International Conference on Mechatronics and Automation ◽

10.1109/icma.2012.6283415 ◽

2012 ◽

Cited By ~ 2

Author(s):

Yu Song ◽

Qingling Li ◽

Yifei Kang ◽

Deli Yan

Keyword(s):

Mobile Robot ◽

Simultaneous Localization And Mapping ◽

Square Root ◽

Localization And Mapping

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PARTICLE FILTER IN SIMULTANEOUS LOCALIZATION AND MAPPING (SLAM) USING DIFFERENTIAL DRIVE MOBILE ROBOT

Jurnal Teknologi ◽

10.11113/jt.v77.6557 ◽

2015 ◽

Vol 77 (20) ◽

Cited By ~ 1

Author(s):

Norhidayah Mohamad Yatim ◽

Norlida Buniyamin

Keyword(s):

Kalman Filter ◽

Mobile Robot ◽

Particle Filter ◽

Mean Squared Error ◽

Simultaneous Localization And Mapping ◽

Simulation Accuracy ◽

Differential Drive ◽

Estimation Algorithms ◽

Localization And Mapping ◽

Number Of Particles

Simultaneous Localization and Mapping (SLAM) problem is a well-known problem in robotics, where a robot has to localize itself and map its environment simultaneously. Particle filter (PF) is one of the most adapted estimation algorithms for SLAM apart from Kalman filter (KF) and Extended Kalman Filter (EKF). In this work, particle filter algorithm has been successfully implemented using a simple differential drive mobile robot called e-puck. The performance of the algorithm implemented is analyzed via varied number of particles. From simulation, accuracy of the resulting maps differed according to the number of particles used. The Root Mean Squared Error (RMSE) of a larger number of particles is smaller compared to a lower number of particles after a period of time.

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