Path Planning and Use of Probabilistic Localization to Keep Track of a Car-Like Robot
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Abstract
This paper presents a particle filter for tracking a robot car. Particle filter is a way of taking variable of interest about something you are trying to measure, and whittling those guesses down by using measurements from sensors. Robot car also uses its sensors to map the region, uses filtering to estimate its position on the road. Robot car need to have awareness of both static and dynamic parts of the environment. Hence, I propose a model based approach which encompasses both path planning and particle filter for tracking robot car.
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Keywords: Particle Filter; Prediction; Monto Carlo localization; Hybride A*, Posterior; Bayes Filter
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