Simultaneous localization and mapping of mobile robot using a RGB-D camera

Localization algorithm based on machine vision is a hot topic in the field of intelligent mobile robot. A fast method for mobile robot 3D SLAM (simultaneous localization and mapping) was presented to address the problem of 3D modeling in complex indoor environment. After filtering, the SIFT feature is extracted and matched between every two frames of the sequence. According to the camera calibration model and the image feature extraction and matching procedure, the association between two 3D point clouds was established. On the basis of the RANSAC (random sample consensus) algorithm, the correspondence based iterative closest point arithmetic model was solved to realize the robot's precise localization effectively. With the key frame-to-frame selection mechanism, the 3D map method and the unique normal characteristic of a spatial point were used for maintaining and updating the global map. Experimental results demonstrate the feasibility and effectiveness of the proposed algorithm in the indoor environment.