Robustifying SLAM using multiple cameras

Abstract: This master thesis is about modifying a certain monocular visual SLAM algorithm to address some of its limitations. The SLAM algorithm is not robust to quick camera motions and input images in which there are few visible features. A second camera and an inertial measurement unit was added to the hardware. Then a method for selecting the appropriate camera for tracking depending on the estimated number of features was implemented to solve the issue of few features. Experiments and results show that this method works well for slow motions. A gyrometer threshold method along with a motion model to solve the issue of quick motions was implemented and reviewed in this thesis.