کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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1140750 | 956740 | 2010 | 18 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Extended Kalman and Particle Filtering for sensor fusion in motion control of mobile robots Extended Kalman and Particle Filtering for sensor fusion in motion control of mobile robots](/preview/png/1140750.png)
Motion control of mobile robots and efficient trajectory tracking is usually based on prior estimation of the robots’ state vector. To this end Gaussian and nonparametric filters (state estimators from position measurements) have been developed. In this paper the Extended Kalman Filter which assumes Gaussian measurement noise is compared to the Particle Filter which does not make any assumption on the measurement noise distribution. As a case study the estimation of the state vector of a mobile robot is used, when measurements are available from both odometric and sonar sensors. It is shown that in this kind of sensor fusion problem the Particle Filter has better performance than the Extended Kalman Filter, at the cost of more demanding computations.
Journal: Mathematics and Computers in Simulation - Volume 81, Issue 3, November 2010, Pages 590–607