Visual simultaneous localization and mapping in a noisy static environment

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dc.contributor.author Makhubela, J. K.
dc.date.accessioned 2021-08-20T04:51:41Z
dc.date.available 2021-08-20T04:51:41Z
dc.date.issued 2019-03
dc.identifier.uri http://hdl.handle.net/10352/462
dc.description M. Tech. (Department of Information and Communication Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology en_US
dc.description.abstract Simultaneous Localization and Mapping (SLAM) has seen tremendous interest amongst the research community in recent years due to its ability to make the robot truly independent in navigation. Visual Simultaneous Localization and Mapping (VSLAM) is when an autonomous mobile robot is embedded with a vision sensor such as monocular, stereo vision, omnidirectional or Red Green Blue Depth (RGBD) camera to localize and map an unknown environment. The purpose of this research is to address the problem of environmental noise, such as light intensity in a static environment, which has been an issue that makes a Visual Simultaneous Localization and Mapping (VSLAM) system to be ineffective. In this study, we have introduced a Light Filtering Algorithm into the Visual Simultaneous Localization and Mapping (VSLAM) method to reduce the amount of noise in order to improve the robustness of the system in a static environment, together with the Extended Kalman Filter (EKF) algorithm for localization and mapping and A* algorithm for navigation. Simulation is utilized to execute experimental performance. Experimental results show a 60% landmark or landfeature detection of the total landmark or landfeature within a simulation environment and a root mean square error (RMSE) of 0.13m, which is minimal when compared with other Simultaneous Localization and Mapping (SLAM) systems from literature. The inclusion of a Light Filtering Algorithm has enabled the Visual Simultaneous Localization and Mapping (VSLAM) system to navigate in an obscure environment. en_US
dc.language.iso en en_US
dc.publisher Vaal University of Technology en_US
dc.subject Simultaneous Localization and Mapping (SLAM) en_US
dc.subject Visual Simultaneous Localization and Mapping (VSLAM) en_US
dc.subject Environmental noise en_US
dc.subject Light intensity en_US
dc.subject Static environment en_US
dc.subject Extended Kalman Filter en_US
dc.subject.lcsh Dissertations, Academic -- South Africa en_US
dc.subject.lcsh Algorithm en_US
dc.subject.lcsh Computer Communication Networks en_US
dc.subject.lcsh Robotics en_US
dc.title Visual simultaneous localization and mapping in a noisy static environment en_US
dc.type Thesis en_US
dc.contributor.supervisor Zuva, Tranos, Prof.
dc.contributor.co-supervisor Nkoana, Tshepiso


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