This paper focuses on developing a robust solution for the Simultaneous Localization and Mapping (SLAM) problem to increase the autonomy of Unmanned Aerial Vehicles (UAV). The investigated topics are related to data fusion, localization, features extraction and matching, map building, 3D pose estimation and SLAM. One important aspect of the autonomous navigation which should be investigated as well as the fusion of data from the different sensor. The data fusion algorithms are very important and their performances are closely dependent on both performances of the constructed map and the accuracy of the UAV position within this map. Optimal and robust filter using centred Gaussian noises is implemented. The original contribution of this work is the proposition and the adaptation of the Extended Kalman Filter (EKF) to solve the Inertial Navigation Systems INS/3D laser UAV navigation problem. Simulation results for 3D flight scenario are presented to demonstrate the advantages of the hybrid localization based on EKF compared with results of the INS relative localization based technique. Good results were obtained with the EKF using noise process and/or measurement noise characteristics, particularly in the case of centred Gaussian noises.


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    Title :

    Simultaneous Localization and Mapping Algorithm based on 3D Laser for Unmanned Aerial Vehicle


    Additional title:

    Lect. Notes Electrical Eng.



    Conference:

    International Conference on Electrical Engineering and Control Applications ; 2019 ; Constantine, Algeria November 19, 2019 - November 21, 2019



    Publication date :

    2020-09-30


    Size :

    18 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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