An efficient loop closure detection method based on spatially constrained feature matching

Loop detection technology is an important part of the simultaneous localization and mapping system for eliminating the pose drift of robots during long-term movement. In order to solve the three main challenges of appearance-based methods, namely viewpoint changes, repeated textures, and large amounts of calculation, this paper proposes an unsupervised loop detection method that takes into account both the texture pattern and position information of feature points and avoids any pre-training steps. Since the relative rotation and translation of the robot between two frames forming loop closure are both very small, the proposed method constrains the matching range with an overlapped block strategy to not only improve the matching precision, but also reduce the cost of matching. Furthermore, the method introduces Gaussian functions to weight and fuse the matching score of each block. The proposed method is evaluated in detail on two different public datasets with various scenarios, and the results show that the proposed method performs better and more efficiently than existing state-of-the-art methods.