Research on loop-closure detection in SLAM based on RGB-D camera

A fast method for mobile robot 3D SLAM (simultaneous localization and mapping) is presented to address the problem of 3D modeling in complex indoor environment. According to the camera calibration model and the image feature extraction and matching procedure, the association between two 3D point clouds can be established. On the basis of the RANSAC (random sample consensus) algorithm, the correspondence based Normal Distribution Transform algorithm (NDT) arithmetic model is solved to realize the robot's initial localization effectively. The loop detection method, which combines the space based method and appearance similarity method, promotes the speed of detection. Contrast experiments based on the actual scene show that, compared with the RGBD-SLAM and RTAB-MAP (realtime appearance-based mapping), the improved hybrid loop-closure detection algorithm has better real-time performance.