scholarly journals Simultaneous Localization and Mapping (SLAM) of a Mobile Robot Based on Fusion of Odometry and Visual Data Using Extended Kalman Filter

10.5772/7801 ◽  
2009 ◽  
Author(s):  
Andre M. ◽  
Adelardo A. D. Medeiros
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Extended Kalman Filter ◽  
Simultaneous Localization And Mapping ◽  
Visual Data ◽  
Localization And Mapping

scholarly journals Simultaneous Localization and Mapping Based on Kalman Filter and Extended Kalman Filter

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Inam Ullah ◽  
Xin Su ◽  
Xuewu Zhang ◽  
Dongmin Choi
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Extended Kalman Filter ◽  
Simultaneous Localization And Mapping ◽  
Absolute Measurement ◽  
Relative Measurement ◽  
Localization Algorithm ◽  
Moving Vehicle ◽  
Localization And Mapping ◽  
Slam Algorithm

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.

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.

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

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.

scholarly journals Straight-lines modelling using planar information for monocular SLAM

2012 ◽  
Vol 22 (2) ◽  
pp. 409-421 ◽  
Author(s):  
André Santana ◽  
Adelardo Medeiros
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Hough Transform ◽  
Simultaneous Localization And Mapping ◽  
Intermediate Stage ◽  
Localization And Mapping ◽  
Monocular Slam ◽  
Real Robot ◽  
Straight Lines ◽  
Indoor And Outdoor

Straight-lines modelling using planar information for monocular SLAMThis work proposes a SLAM (Simultaneous Localization And Mapping) solution based on an Extended Kalman Filter (EKF) in order to enable a robot to navigate along the environment using information from odometry and pre-existing lines on the floor. These lines are recognized by a Hough transform and are mapped into world measurements using a homography matrix. The prediction phase of the EKF is developed using an odometry model of the robot, and the updating makes use of the line parameters in Kalman equations without any intermediate stage for calculating the distance or the position. We show two experiments (indoor and outdoor) dealing with a real robot in order to validate the project.

scholarly journals Simultaneous localization and mapping: A feature-based probabilistic approach

2009 ◽  
Vol 19 (4) ◽  
pp. 575-588 ◽  
Author(s):  
Piotr Skrzypczyński
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Data Association ◽  
Probabilistic Approach ◽  
Simultaneous Localization And Mapping ◽  
Probabilistic Formulation ◽  
Recent Advances ◽  
Localization And Mapping ◽  
Feature Based ◽  
Points Of View

Simultaneous localization and mapping: A feature-based probabilistic approachThis article provides an introduction to Simultaneous Localization And Mapping (SLAM), with the focus on probabilistic SLAM utilizing a feature-based description of the environment. A probabilistic formulation of the SLAM problem is introduced, and a solution based on the Extended Kalman Filter (EKF-SLAM) is shown. Important issues of convergence, consistency, observability, data association and scaling in EKF-SLAM are discussed from both theoretical and practical points of view. Major extensions to the basic EKF-SLAM method and some recent advances in SLAM are also presented.

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