Extended Kalman Filter Based Mobile Robot Localization in Indoor Fire Environments

Kim, Jong-Hwan; Kim, Gun In

doi:10.18178/ijmerr.5.1.62-66

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…One of the significant issues for robots as the robots keeps track of their position by holding an outline of areas and an assessment of their localization is navigation. In addition, data from a Frequency-Modulated Continuous-Wave (FMCW) Radar, Inertial Measurement Unit (IMU) and encoders that are capable of withstanding fire environments were fused to localize the robot in indoor fire environments [1]. The SLAM is the most generic widely investigated main subfields of mobile robots.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Unscented Kalman Filter for Robot Navigation Problem (Adaptive Unscented Kalman Filter Using Incorporating Intuitionistic Fuzzy Logic for Concurrent Localization and Mapping)

et al. 2022

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The navigation of a mobile robot is a very important issue, especially for an autonomous mobile robot. A robot autonomously can track the area by interpreting the arena, building an adequate map, and localizing itself to this map. This paper proposes a Hybrid filter for Concurrent Localization and Mapping (CLAM) in the navigation to rectify the faults, basically Unscented Fast Simultaneous Localization and Mapping (SLAM) (UFS). We also interrogate the effectiveness of the IF system to investigate nonlinear attributes. A probabilistic method has planned the solution to the CLAM issue, which is an essential requirement for the navigation of robots. The Hybrid filter CLAM contains an Intuitionistic Fuzzy Logic (IFL) and Unscented Kalman Filter (UKF). The IFL is first ordered by using a correctness function explained on score functions for the non-membership function (NMF) and membership function (MF) of the IFL. Then this ordering is utilized to develop a method for a sufficient decision on the CLAM issue. The proposed method has a few privileges in management robot navigation with nonlinear movements owing to the inference feature of the IFL, which also needs a fewer quantity of comparisons than the UFS and shows much better efficiency from the robustness, perspective assessment exactitude, and reliability than the UFS, also, for learning the measurement and control noise covariance matrices for increasing correctness and consistency are utilized IFL. The Hybrid filter CLAM proficiency compared with the UFS has a smaller quantity of computations and good efficiency for bigger areas as demonstrate in the results of simulation and experimental.

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Section: Introductionmentioning

confidence: 99%

Adaptive Unscented Kalman Filter for Robot Navigation Problem (Adaptive Unscented Kalman Filter Using Incorporating Intuitionistic Fuzzy Logic for Concurrent Localization and Mapping)

et al. 2022

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“…To realize mobile robot self-navigation, many approaches can be chosen [5,6]. e inertial navigation system (INS) and the vision navigation system (VNS) are the two most commonly used schemes which have gradually become standard sensors on mobile robot systems [7,8]. For example, an INS is used to achieve the self-navigation in a work [9], while a visual navigation system is used in another work's navigation tasks [10].…”

Section: Introductionmentioning

confidence: 99%

“…us, many approaches are proposed to improve data fusion. For instance, extended Kalman filter (EKF) is designed for the mobile robot localization in indoor environments [8]. However, the stronger a nonlinear problem is, the more nonignorable the high-order terms obtained by the expansion of Taylor series are.…”

Section: Introductionmentioning

confidence: 99%

Indoor Vision/INS Integrated Mobile Robot Navigation Using Multimodel-Based Multifrequency Kalman Filter

Liu

Sun

et al. 2021

Mathematical Problems in Engineering

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In order to further improve positioning accuracy, this paper proposes an indoor vision/INS integrated mobile robot navigation method using multimodel-based multifrequency Kalman filter. Firstly, to overcome the insufficient accuracy of visual data when a robot turns, a novel multimodel integrated scheme has been investigated for the mobile robots with Mecanum wheels which can make fixed point angled turns. Secondly, a multifrequency Kalman filter has been used to fuse the position information from both the inertial navigation system and the visual navigation system, which overcomes the problem that the filtering period of the integrated navigation system is too long. The proposed multimodel multifrequency Kalman filter gives the root mean square error (RMSE) of 0.0184 m in the direction of east and 0.0977 m in north, respectively. The RMSE of visual navigation system is 0.8925 m in the direction of east and 0.9539 m in north, respectively. Experimental results show that the proposed method is effective.

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GPU-based multilayer invariant EKF for camera localization

Huang

Zhang

2018

Computers & Graphics

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Extended Kalman Filter Based Mobile Robot Localization in Indoor Fire Environments

Cited by 6 publications

References 22 publications

Adaptive Unscented Kalman Filter for Robot Navigation Problem (Adaptive Unscented Kalman Filter Using Incorporating Intuitionistic Fuzzy Logic for Concurrent Localization and Mapping)

Adaptive Unscented Kalman Filter for Robot Navigation Problem (Adaptive Unscented Kalman Filter Using Incorporating Intuitionistic Fuzzy Logic for Concurrent Localization and Mapping)

Indoor Vision/INS Integrated Mobile Robot Navigation Using Multimodel-Based Multifrequency Kalman Filter

GPU-based multilayer invariant EKF for camera localization

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