Loop closure detection with patch-level local features and visual saliency prediction

Loop closure detection (LCD) is essential in the field of visual Simultaneous Localization and Mapping (vSLAM). In the LCD system, geometrical verification based on image matching plays a crucial role in avoiding erroneous detections. This paper focuses on adopting patch-level local features for image matching to compute the similarity score between the current query image and the candidate images. However, an important factor that may reduce the robustness is that some distracting and dynamic regions in a scene (e.g., the sky, cars, pedestrians, the ground, etc.) are not helpful and may seriously harm the performance. To address this challenge, we first use a newly designed patch descriptor loss to optimize the distance relationship between the patch-level local features. In this way, the patch-level local features extracted from the query/candidate images are more suitable for performing image matching. Moreover, we mimic the visual attention mechanism and propose a patch matching with saliency strategy, which enables local patches in salient regions to play crucial roles in image matching by assigning suitable weights to them. Finally, experiments on several public datasets demonstrate that the proposed LCD system can achieve encouraging improvements over the state-of-the-art approaches regarding recall rates under 100% precision. •Create a novel LCD-oriented saliency prediction dataset (Saliency-LCD).•Design SaliencyNetVLAD to extract patch-level local features and global features.•Patch-level local features are optimized by using the novel patch descriptor loss.•Use the predicted saliency map to improve the geometrical verification process.