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A non-invasive speed and position sensor for induction machines using external search coils

Keysan, Ozan
For an autonomous mobile robot, localization and map building are vital capabilities. The localization ability provides the robot location information, so the robot can navigate in the environment. On the other hand, the robot can interact with its environment using a model of the environment (map information) which is provided by map building mechanism. These two capabilities depends on each other and simultaneous operation of them is called SLAM (Simultaneous Localization and Map Building). While various sensors are used for this algorithm, vision-based approaches are relatively new and have attracted more interest in recent years. In this thesis work, a versatile Visual SLAM system is constructed and presented. In the core of this work is a vision-based simultaneous localization and map building algorithm which uses point features in the environment as visual landmarks and Extended Kalman Filter for state estimation. A detailed analysis of this algorithm is made including state estimation, feature extraction and data association steps. The algorithm is extended to be used for both stereo and single camera systems. The core of both algorithms is same and we mention the differences of both algorithms originated from the measurement dissimilarity. The algorithm is run also in different motion modes, namely predefined, manual and autonomous. Secondly, a map management strategy is developed especially for extended environments. When the robot runs the SLAM algorithm in large environments, the constructed map contains a great number of landmarks obviously. The efficiency algorithm takes part, when the total number of features exceeds a critical value for the system. In this case, the current map is rarefied without losing the geometrical distribution of the landmarks. Furthermore, a well-organized graphical user interface is implemented which enables the operator to select operational modes, change various parameters of the main SLAM algorithm and see the results of the SLAM operation both textually and graphically. Finally, a basic mission concept is defined in our system, in order to illustrate what robot can do using the outputs of the SLAM algorithm. All of these ideas mentioned are implemented in this thesis, experiments are conducted using a real robot and the analysis results are discussed by comparing the algorithm outputs with ground-truth measurements.