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

Hung, Cong; Lin, Huei-Yung

doi:10.1177/1729881419874645

Cited by 13 publications

(11 citation statements)

References 31 publications

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“…In [45], the authors presented a neurofuzzy-based adaptive EKF method. The purpose of this method is to estimate the right value of matrix R at every stage.…”

Section: Comparison Of the Proposed And Other Algorithmsmentioning

confidence: 99%

“…The typical EKF algorithm has a problem that machine noise and the prior statistical characteristics of the observed noise cannot be predicted accurately. Thus, the authors presented an enhanced EKF algorithm to accomplish a fuzzy adaptive SLAM [45,47,48]. Therefore, to predict the position, a laser matching is applied to the EKF prediction process, and the weighted average location is used as the final location of the predicted component.…”

Section: Comparison Of the Proposed And Other Algorithmsmentioning

confidence: 99%

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

Ullah

Zhang

et al. 2020

Wireless Communications and Mobile Computing

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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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“…In [45], the authors presented a neurofuzzy-based adaptive EKF method. The purpose of this method is to estimate the right value of matrix R at every stage.…”

Section: Comparison Of the Proposed And Other Algorithmsmentioning

confidence: 99%

Section: Comparison Of the Proposed And Other Algorithmsmentioning

confidence: 99%

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

Ullah

Zhang

et al. 2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

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“…The first approach is based on the probability estimation methods to calculate the robot's position. The Kalman filter is frequently used to find the largest probability and update the location state based on the sensor data [8][9][10]. In the previous work, Negenborn illustrated a method using the distributed Kalman filter algorithm with a distributed model predictive control (MPC) scheme for large-scale, multi-rate systems [11].…”

Section: Introductionmentioning

confidence: 99%

Mobile Robot Self-Localization Using Omnidirectional Vision with Feature Matching from Real and Virtual Spaces

Lin

2021

Applied Sciences

Self Cite

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This paper presents a novel self-localization technique for mobile robots based on image feature matching from omnidirectional vision. The proposed method first constructs a virtual space with synthetic omnidirectional imaging to simulate a mobile robot equipped with an omnidirectional vision system in the real world. In the virtual space, a number of vertical and horizontal lines are generated according to the structure of the environment. They are imaged by the virtual omnidirectional camera using the catadioptric projection model. The omnidirectional images derived from the virtual and real environments are then used to match the synthetic lines and real scene edges. Finally, the pose and trajectory of the mobile robot in the real world are estimated by the efficient perspective-n-point (EPnP) algorithm based on the line feature matching. In our experiments, the effectiveness of the proposed self-localization technique was validated by the navigation of a mobile robot in a real world environment.

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“…It has the ability to define complex nonlinear equations with a simple linguistic rule base. Most previous research studies [6][7][8] have been based on matching theoretical and actual covariance based on the difference, but this method does not give accurate results in the heavy outliers in the stochastic part of the model.…”

Section: Introductionmentioning

confidence: 99%

Underwater Vehicle Positioning by Correntropy-Based Fuzzy Multi-Sensor Fusion

Shaukat

Moinuddin

Otero

2021

Sensors

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The ability of the underwater vehicle to determine its precise position is vital to completing a mission successfully. Multi-sensor fusion methods for underwater vehicle positioning are commonly based on Kalman filtering, which requires the knowledge of process and measurement noise covariance. As the underwater conditions are continuously changing, incorrect process and measurement noise covariance affect the accuracy of position estimation and sometimes cause divergence. Furthermore, the underwater multi-path effect and nonlinearity cause outliers that have a significant impact on positional accuracy. These non-Gaussian outliers are difficult to handle with conventional Kalman-based methods and their fuzzy variants. To address these issues, this paper presents a new and improved adaptive multi-sensor fusion method by using information-theoretic, learning-based fuzzy rules for Kalman filter covariance adaptation in the presence of outliers. Two novel metrics are proposed by utilizing correntropy Gaussian and Versoria kernels for matching theoretical and actual covariance. Using correntropy-based metrics and fuzzy logic together makes the algorithm robust against outliers in nonlinear dynamic underwater conditions. The performance of the proposed sensor fusion technique is compared and evaluated using Monte-Carlo simulations, and substantial improvements in underwater position estimation are obtained.

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

Cited by 13 publications

References 31 publications

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

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

Mobile Robot Self-Localization Using Omnidirectional Vision with Feature Matching from Real and Virtual Spaces

Underwater Vehicle Positioning by Correntropy-Based Fuzzy Multi-Sensor Fusion

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