Loop Closure Detection Based on Image Semantic Segmentation in Indoor Environment

Abstract

When mobile robots run in indoor environment, a large number of similar images are easy to appear in the images collected, probably causing false-positive judgment in loop closure detection based on simultaneous localization and mapping (SLAM). To solve this problem, a loop closure detection algorithm for visual SLAM based on image semantic segmentation is proposed in this paper. Specifically, the current frame is semantically segmented by optimized DeepLabv3+ model to obtain semantic labels in the image. The 3D semantic node coordinates corresponding to each semantic label are then extracted by combining mask centroid and image depth information. According to the distribution of semantic nodes, the DBSCAN density clustering algorithm is adopted to cluster densely distributed semantic nodes to avoid mismatching due to the close distance of semantic nodes in the subsequent matching process. Finally, the multidimensional similarity comparison of first rough and then fine is adopted to screen the candidate frames of loop closure from key frames and then confirm the real loop closure to complete accurate loop closure detection. Testing with public datasets and self-filmed datasets, experimental results show that being well adapted to illumination change, viewpoint deviation, and item movement or missing, the proposed algorithm can effectively improve the accuracy of loop closure detection in indoor environment.