Robots are becoming increasingly available and capable, are becoming part of everyday life in applications: robots that guide blind or mentally handicapped people, robots that clean large office buildings and department stores, robots that assist people in shopping, recreational activities, etc.Localization, in the sense of understanding accurately one's position in the environment, is a basic building block for performing important tasks. Therefore, there is an interest in having robots to perform autonomously and accurately localization tasks in highly cluttered and dynamically changing environments.To perform localization, robots are required to opportunely combine their sensors measurements, sensors models and environment model. In this thesis we aim at improving the tools that constitute the basis of all the localization techniques, that are the models of these sensors, and the algorithms for processing the raw information from them. More specifically we focus on:- finding advanced statistical models of the measurements returned by common laser scanners (a.k.a. Lidars), starting from both physical considerations and evidence collected with opportune experiments;- improving the statistical algorithms for treating the signals coming from these sensors, and thus propose new estimation and system identification techniques for these devices.In other words, we strive for increasing the accuracy of Lidars through opportune statistical processing tools.The problems that we have to solve, in order to achieve our aims, are multiple. The first one is related to temperature dependency effects: the laser diode characteristics, especially the wave length of the emitted laser and the mechanical alignment of the optics, change non-linearly with temperature. In one of the papers in this thesis we specifically address this problem and propose a model describing the effects of temperature changes in the laser diode; these include, among others, the presence of multi-modal measurement noises. Our contributions then include an ...


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    Title :

    Statistical Calibration Algorithms for Lidars


    Contributors:

    Publication date :

    2016-01-01


    Remarks:

    Local 77e5456a-bb9e-4fb1-b72b-ef40b6a0142a


    Type of media :

    Theses


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



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