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The goal of this project is to explore both the theory behind the Extended Kalman Filter and the way it was used to localize a four-wheeled mobile-robot. This can be achieved by estimating in real-time the pose of the robot, while using a pre-acquired map through Laser Range Finder (LRF). The LRF is used to scan the environment, which is represented through line segments. Through a prediction step, the robot simulates its kinematic model to predict his current position. In order to minimize the difference between the matched lines from the global and local maps, a update step is implemented. It should be noted that every measurement has associated uncertainty that needs to be taken into account when performing each step of the Extended Kalman Filter. These uncertainties, or noise, are described by covariance matrices that play a very important role in the algorithm. Since we are dealing with an indoor structured environment, mainly composed by walls and straight-edged objects, the line segment representation of the maps was the chosen method to approach the problem.
This is an unofficial LaTeX thesis template for Abertay University, Dundee, UK.
The template is based on:
Abertay University Word Template.
University of Oxford Mathematical Institute PhD Thesis Template.
University of Glasgow Thesis Template.
Modèle de rapport ASD v1
Ludovic Journaux 2017-2018
AgroSup Dijon
Ce modèle est destiné aux étudiants d'AgroSup Dijon pour leur rapports et mémoire de 3eme année.
The package formats articles using the MLA style. The aim is that students and other academics in the humanities should be able to typeset their materials, properly, with minimal effort on their part.
Ryan Aycock, Steven Wheelwright
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