Abstract
This thesis presents an algorithm of robot simultaneous localization and mapping (SLAM) using a RGB-D sensor. This research consists of four stages: first, the Kinect RGB-D sensor is calibrated including the intrinsic parameters of RGB camera as well as the alignment of the RGB sensor and the depth sensor. The RGB-D SLAM is developed and implemented in indoor environments at the second stage. Third, the task of structure from motion (SFM) is integrated with the RGB-D SLAM to construct the environment model. Computational speed is improved at the last stage. The concept cloud computing is applied to the SLAM system by dividing the system into two procedures including image processing and state estimation. The procedure of image processing is remained at the mobile sensory system, while the state estimation is implemented by a cloud computing server. Experimental results show that the computational speed is increased 15% with the cloud computing.
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