Iccas 2010 2010
DOI: 10.1109/iccas.2010.5669779
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Sensor data fusion using Unscented Kalman Filter for accurate localization of mobile robots

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Cited by 19 publications
(8 citation statements)
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“… Simultaneous Localization and Mapping (SLAM) [56].  Unscented Kalman Filter [43].  Boiler Header Inspection Robot (BHIR) [58].…”
Section: Inspectionmentioning
confidence: 99%
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“… Simultaneous Localization and Mapping (SLAM) [56].  Unscented Kalman Filter [43].  Boiler Header Inspection Robot (BHIR) [58].…”
Section: Inspectionmentioning
confidence: 99%
“… Simultaneous Localization and Mapping (SLAM) [56].  Unscented Kalman Filter [43].  Inspection of Circumferential Pipe Parts [44], [145].…”
Section: Image Analysis 77%mentioning
confidence: 99%
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“…This filter was first used to solve trajectory tracking problems in preparation of the Apollo flights. It has become an essential algorithm for the trajectory processing in modern radar systems and other engineering applications [18][19][20][21][22][23][24][25][26][27]. In this paper, the application of KF plays an indispensable role, which aims to reduce the measurement noise of the used sensors in the proposed prototype, and through it, we can stabilize the operation of system in order to make it more efficient.…”
Section: Kalman Filter Algorithmmentioning
confidence: 99%
“…However, the EKF based fusion algorithms involve local linearization of the system. In order to explicitly account for the nonlinearities, involved in the dynamical model, few progressive approaches consider the Unscented Kalman Filter (UKF) [12,13] and the Particle Filter (PF) [14] based multi-sensor fusion for robotic localization. Even though the UKF and PF are superior for addressing nonlinearities, the advantage comes with additional complexity in the computational burden.…”
Section: Introductionmentioning
confidence: 99%