@MASTERSTHESIS\{IMM2016-06940,
    author       = "D. Tranos",
    title        = "State estimation on non-linear Autonomous Guided Vehicles",
    year         = "2016",
    school       = "Technical University of Denmark, Department of Applied Mathematics and Computer Science",
    address      = "Richard Petersens Plads, Building 324, {DK-}2800 Kgs. Lyngby, Denmark, compute@compute.dtu.dk",
    type         = "",
    note         = "{DTU} supervisors: Associate Professors Niels Kj{\o}lstad Poulsen (Main supervisor), nkpo@dtu.dk, Department of Applied Mathematics and Computer Science, and Ole Ravn, Department of Electrical Engineering",
    url          = "http://www.compute.dtu.dk/English.aspx",
    abstract     = "This thesis deals with the application of nonlinear state estimation techniques for the localization of an Autonomous Guided Vehicle, operating in an orchard environment. Because {GPS} availability is not guaranteed, the localization relies solely on odometry and measurements taken by a laser scanner.

Suitable models are derived to generate the vehicle’s motion, the sensor measurements and the environment; they are then used for the construction of a simulation platform. Subsequently, three contemporary nonlinear filters are studied as possible solutions, the Unscented Kalman Filter, the Particle Filter and the Unscented Particle Filter.

These filters are implemented in the simulation platform and their ability to localize the vehicle is assessed, with respect to estimation accuracy and computational effort, in two scenarios; one in which the environment is modeled correctly and another where there exist discrepancies between the environment and the model."
}