关键词: RGB-D SLAM, ORB特征, 透视N点算法, 视觉词典, 姿态图优化

Abstract: SLAM is the current research hot spot in robot area and is considered to be the key of achieving robot’s full autonomous movement. Traditional RGB-D SLAM algorithms compute camera’s pose via SIFT descriptor. On account of the complexity of extracting SIFT descriptor, siftGPU is applied to accelerate this procedure, which makes it unsuitable for embedded equipment. Besides, traditional algorithm is inefficient in loop closure detection and is bad at instantaneity. Therefore, a new approach combined ORB feature and visual dictionary is proposed. In the front end of the algorithm, ORB feature of the adjacent images is firstly extracted and then the nearest neighbor and the second nearest neighbor is found by k-Nearest Neighbor（kNN） algorithm. Once found, ratio test and cross test is adopted to remove outliers. Secondly, a modified PROSAC-PnP algorithm is used to calculate the high-accuracy estimation of camera’s pose. In the back end, a loop closure detection algorithm based on visual dictionary is performed to reduce accumulated error of the robot’s movement, which adds new constraint to pose graph. Finally, generalized graph optimization tool is used to perform global pose optimization and global consistent camera pose and point cloud is got. The test and comparison on the standard dataset shows that this algorithm can obtain better robustness.

Key words: RGB-D SLAM, ORB feature, Progressive Sample Consensus based Perspective-N-Point（PROSAC-PnP）, visual dictionary, pose graph optimization