A Loop Closure Detection Algorithm for Robots Based on Region Proposals of Interest of Image

The loop closure detection algorithm of robots based on deep learning shows certain robustness under complex illumination, but it is prone to be affected by the scene with obvious changes of view angle. Therefore, a new loop closure detection method using RPOIs (region proposals of interest) of image is proposed. Firstly, the RPOIs of image are obtained by the improved MSRPN (multi-scale region proposal network), and the feature of interested region proposals is extracted by the improved PlaceCNN (Place dataset based convolutional neural network). Then, considering the shape similarity of region proposals, a loop closure detection algorithm based on RPOI_PlaceCNN (RPOI based PlaceCNN) is proposed by adopting the principle of coarse matching firstly and fine matching secondly. The space constraint between bidirectional matching pairs is used to remove incorrect matching pairs, which can improve the overall accuracy of loop closure detection. The effectiveness of the proposed method is experimentally verified on three public datasets, i.e. GardensPoint, Mapillary and Norland datasets. The experimental results show that the loop closure detection algorithm proposed can exhibit strong robustness in the situations of significant scene changes caused by illumination, view angle and different combinations of changes.