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

Ullah, Inam; Su, Xin; Zhang, Xuewu; Choi, Dongmin

doi:10.1155/2020/2138643

Cited by 34 publications

(24 citation statements)

References 49 publications

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“…Ullah, et al [28] have developed two SLAM algorithms for robot localization, the first one is based on the linear KF and the second one is based on the EKF. Although EKF is one of the most common filtering techniques, it has some disadvantages like being difficult to implement in practice.…”

Section: A) Extended Kalman Filter Ekfmentioning

confidence: 99%

The Simultaneous Localization and Mapping (SLAM)-An Overview

Alsadik

Karam²

2021

JASTT

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Positioning is a need for many applications related to mapping and navigation either in civilian or military domains. The significant developments in satellite-based techniques, sensors, telecommunications, computer hardware and software, image processing, etc. positively influenced to solve the positioning problem efficiently and instantaneously. Accordingly, the mentioned development empowered the applications and advancement of autonomous navigation. One of the most interesting developed positioning techniques is what is called in robotics as the Simultaneous Localization and Mapping SLAM. The SLAM problem solution has witnessed a quick improvement in the last decades either using active sensors like the RAdio Detection And Ranging (Radar) and Light Detection and Ranging (LiDAR) or passive sensors like cameras. Definitely, positioning and mapping is one of the main tasks for Geomatics engineers, and therefore it's of high importance for them to understand the SLAM topic which is not easy because of the huge documentation and algorithms available and the various SLAM solutions in terms of the mathematical models, complexity, the sensors used, and the type of applications. In this paper, a clear and simplified explanation is introduced about SLAM from a Geomatical viewpoint avoiding going into the complicated algorithmic details behind the presented techniques. In this way, a general overview of SLAM is presented showing the relationship between its different components and stages like the core part of the front-end and back-end and their relation to the SLAM paradigm. Furthermore, we explain the major mathematical techniques of filtering and pose graph optimization either using visual or LiDAR SLAM and introduce a summary of the deep learning efficient contribution to the SLAM problem. Finally, we address examples of some existing practical applications of SLAM in our reality.

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Section: A) Extended Kalman Filter Ekfmentioning

confidence: 99%

The Simultaneous Localization and Mapping (SLAM)-An Overview

Alsadik

Karam²

2021

JASTT

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“…Similar to equation (18), equation (33) illustrates that when the force F e is applied to the EE of the robot and its Complexity gravity is considered, the radial torque at the joint will lead to the positioning error 0 ΔP r(3×1) .…”

Section: Modelling Of Radial Deformationmentioning

confidence: 99%

“…Finally, since C a and C r are the integrated stiffness, they cannot be obtained directly by measurement. However, many mathematical methods can be used to identify C a and C r , e.g., least square method [29,30], genetic algorithm [31], particle swarm optimization algorithm [32], Kalman filtering algorithm [33], etc.…”

Section: Error Modelling Including Axial and Radial Deformationmentioning

confidence: 99%

Positioning Error Compensation for Industrial Robots Based on Stiffness Modelling

Gao

Liu

2020

Complexity

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Insufficient stiffness of industrial robots is a significant factor which affects its positioning accuracy. To improve the positioning accuracy, a novel positioning error compensation method based on the stiffness modelling is proposed in this paper. First, the positioning errors considering the end load and gravity of industrial robots due to stiffness are analyzed. Based on the results of analysis, it is found that the positioning errors can be described by two kinds of deformation errors at joints: the axial deformation error and the radial deformation error. Then, the axial deformation error is modelled by the differential relationship of kinematics equations. The model of radial deformation error is deduced through the recurrence method and rotation transformation between joints. Finally, these two models are transformed into a Cartesian coordinate system, and a positioning error compensation method based on these two models is presented. Simulations based on the finite element analysis are implemented to verify the positioning error compensation method. The results show that the suggested method can efficiently predict the positioning error according to the gravity and loads, so that the positioning accuracy of industrial robots can be improved with the proposed method.

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“…KF is only appli-cable for the linear stochastic procedures; however, for the nonlinear procedures, the EKF can be applied. The supposition of these two methods (KF and EKF) is that noise and process measurements are self-governing and with a normal probability distribution [8].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Localization by Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter-Based Techniques

Ullah

Zhu

et al. 2020

Wireless Communications and Mobile Computing

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Mobile robot localization has attracted substantial consideration from the scientists during the last two decades. Mobile robot localization is the basics of successful navigation in a mobile network. Localization plays a key role to attain a high accuracy in mobile robot localization and robustness in vehicular localization. For this purpose, a mobile robot localization technique is evaluated to accomplish a high accuracy. This paper provides the performance evaluation of three localization techniques named Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF). In this work, three localization techniques are proposed. The performance of these three localization techniques is evaluated and analyzed while considering various aspects of localization. These aspects include localization coverage, time consumption, and velocity. The abovementioned localization techniques present a good accuracy and sound performance compared to other techniques.

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

Cited by 34 publications

References 49 publications

The Simultaneous Localization and Mapping (SLAM)-An Overview

The Simultaneous Localization and Mapping (SLAM)-An Overview

Positioning Error Compensation for Industrial Robots Based on Stiffness Modelling

Evaluation of Localization by Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter-Based Techniques

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