Extended kalman filter and stereoscopic vision based autonomous flying system for quadcopters

dc.contributor.advisorChandima, DP
dc.contributor.advisorJayasekara, AGBP
dc.contributor.authorSomasiri, JAAS
dc.date.accept2018-06
dc.date.accessioned2018-11-21T20:12:10Z
dc.date.available2018-11-21T20:12:10Z
dc.description.abstractThis thesis can be divided into two main modules. First module is implementation of an Ex-tended Kalman filter and introduce into existing flight control algorithm which is used to con-trol multi-rotor unmanned vehicles. Purpose of this implementation is to improve flight per-formance and reliability of the system. Second module is implementation of an obstacle avoid-ance system based on stereo vision and fuzzy logic for same flight control algorithm to avoid crashes and avoid obstacles during navigation. In this thesis Chapter 1 introduce basic modules of this implementations and explain about flight control algorithm and its major components which is used in here. This chapter also explains the theory behind the Extended Kalman Fil-ters, stereo vision systems and fuzzy logic. Chapter 2 described literature survey about existing implementation of Extended Kalman filters on multi-rotor platforms, stereo vision system im-plementations and related obstacle avoidance implementations like artificial potential field and fuzzy logic. First section of chapter 3 focused into implementation details and experimenting results of Extended Kalman filter and also explained how Extended Kalman filter outputs are combined to Attitude and Position controllers of flight control algorithm. Second section of chapter 3 focused into implementation and experimenting results of the stereo vision system. This section explained detail implementation of stereo vision system like stereo camera cali-bration, image rectification, disparity map generation and depth calculation. Mainly OpenCV was used in this implementation. Third section of chapter 3 focused into explained implemen-tation of fuzzy decision-making system. In here described deciding of fuzzy inputs and outputs using depth image, creation of fuzzy inference system, selection of membership functions and combined fuzzy decision-making system with flight control algorithm. Flight testing and ex-perimental results of Extended Kalman filter and obstacle avoidance system were described in chapter 4, both systems were tested on outdoor environments and improvement of the perfor-mance and reliability was discussed in this chapter. Chapter 5 is the final chapter of this thesis and it includes conclusion of the thesis, recommendations and further works.en_US
dc.identifier.accnoTH3631en_US
dc.identifier.citationSomasiri, J.A.A.S. (2018). Extended kalman filter and stereoscopic vision based autonomous flying system for quadcopters [Master’s theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.mrt.ac.lk/handle/123/13698
dc.identifier.degreeMaster of Science in Industrial Automationen_US
dc.identifier.departmentDepartment of Electrical Engineeringen_US
dc.identifier.facultyEngineeringen_US
dc.identifier.urihttp://dl.lib.mrt.ac.lk/handle/123/13698
dc.language.isoenen_US
dc.subjectELECTRICAL ENGINEERING-Dissertationen_US
dc.subjectINDUSTRIAL AUTOMATION-Dissertationen_US
dc.subjectQUADCOPTERSen_US
dc.subjectUNMANNED VEHICLESen_US
dc.subjectFLIGHT PERFORMANCEen_US
dc.subjectOBSTACLE AVOIDANCE
dc.subjectKALMAN FILTERS
dc.titleExtended kalman filter and stereoscopic vision based autonomous flying system for quadcoptersen_US
dc.typeThesis-Full-texten_US

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