Loop Closure Detection of Visual SLAM Based on Point and Line Features

For traditional loop closure detection algorithm, only using the vectorization of point features to build visual dictionary is likely to cause perceptual ambiguity. In addition, when scene lacks texture information, the number of point features extracted from it will be small and cannot describe the image effectively. Therefore, this paper proposes a loop closure detection algorithm which combines point and line features. To better recognize scenes with hybrid features, the building process of traditional dictionary tree is improved in the paper. The features with different flag bits were clustered separately to construct a mixed dictionary tree and word vectors that can represent the hybrid features, which can better describe structure and texture information of scene. To ensure that the similarity score between images is more reasonable, different similarity coefficients were set in different scenes, and the candidate frame with the highest similarity score was selected as the candidate closed loop. Experiments show that the point-line comprehensive feature was superior to the single feature in the structured scene and the strong texture scene, the recall rate of the proposed algorithm was higher than the state-of-the-art methods when the accuracy is 100%, and the algorithm can be applied to more diverse environments.