Adaptive neuro fuzzy supported Kalman filter approach for simultaneous localization and mapping

Ankishan, Haydar; Efe, Murat

doi:10.1109/siu.2011.5929638

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…One effective solution to deal with this issue is to employ adaptive algorithms for EKF SLAM. 9,10,11,12,13 Specifically, the studies of the literature 14,15 have shown that the approach of artificial intelligence-assisted EKF for the SLAM problems is far more superior than the conventional approaches (e.g. unscented filter, square root unscented filter) and others (e.g.…”

Section: Introductionmentioning

confidence: 99%

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

Hung

Lin

2019

International Journal of Advanced Robotic Systems

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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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Section: Introductionmentioning

confidence: 99%

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

Hung

Lin

2019

International Journal of Advanced Robotic Systems

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“…In 2010, Ankışhan H. and Efe M. compared the results of simultaneous positioning and mapping of the square root odorless and square root difference from the Kalman Filters to those obtained from the Extended Kalman Filter and produced an alternative solution by saving the margin of error and time spent (Ankışhan & Efe, 2010).…”

Section: Introductionmentioning

confidence: 99%

Otonom Robotlarla Lokalizasyon ve Nokta Bulutu Tabanlı 3B Haritalama

Açıkel¹,

Gökçen²

2019

European Journal of Science and Technology

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In this study, localization and environment mapping application is aimed with an autonomous robot. A new algorithm is presented to scan a larger area, to produce faster and more accurate results. The mapping process is intended not to be affected by environmental movements. It can be used in military areas to gain manpower in the mine area or to model the environment in virtual reality applications. In autonomous robot design, the horizontal and vertical angle values of the Lidar Lite V3 are provided by two servo motors. A fourwheeled car model was used. Ultrasonic sensors are placed on the front, right and left surfaces of the robot, Raspberry Pi 3 and Pi Camera was placed on top. It is seen that the moving average filter removes the noise generated on the map. The Lidar Lite V3 was able to take measurements at longer distances. Noise generation is prevented by motion detection algorithm. It can be used in interior space mapping, environment modeling, virtual reality applications, military areas, mining sector and graphic applications. In outdoor mapping, it can be used to create a map of an area of 40 meters in diameter. The mapping process was performed as close to the actual values by using the moving average filter and the Lidar Lite V3. The mapping process with the motion detection system is paused and actual position data are obtained using GPS.

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Probabilistic Bases

2013

Simultaneous Localization and Mapping for Mobile Robots

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This is the third chapter of the first section. It is a compendium of all the concepts and theorems of probability theory that are found in the problems of Bayesian estimation of a robot location and the map of its environment. It presents uncertainty as an intrinsic feature of any mobile robot that develops in a real environment. It is then discussed how uncertainty has been treated along the history of science and how probabilistic approaches have represented such a huge success in many engineering fields, including robotics. The fundamental concepts of probability theory are discussed along with some advanced topics needed in further chapters, following a learning curve as smooth and comprehensive as possible.

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Adaptive neuro fuzzy supported Kalman filter approach for simultaneous localization and mapping

Cited by 3 publications

References 7 publications

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

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

Otonom Robotlarla Lokalizasyon ve Nokta Bulutu Tabanlı 3B Haritalama

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