Robust loop-closure detection is essential for visual SLAM. Traditional methods often focus on the geometric and visual features in most scenes but ignore the semantic information provided by objects. Based on this consideration, we present a strategy that models the visual scene as semantic sub-graph by only preserving the semantic and geometric information from object detection. To align two sub-graphs efficiently, we use a sparse Kuhn–Munkres algorithm to speed up the search for correspondence among nodes. The shape similarity and the Euclidean distance between objects in the 3-D space are leveraged unitedly to measure the image similarity through graph matching. Furthermore, the proposed approach has been analyzed and compared with the state-of-the-art algorithms at several datasets as well as two indoor real scenes, where the results indicate that our semantic graph-based representation without extracting visual features is feasible for loop-closure detection at potential and competitive precision.

强大的闭环检测对于视觉SLAM是必不可少的。传统方法通常将注意力集中在大多数场景中的几何和视觉特征上，而忽略了对象提供的语义信息。基于此考虑，我们提出了一种仅通过保留对象检测中的语义和几何信息将视觉场景建模为语义子图的策略。为了有效地对齐两个子图，我们使用稀疏的Kuhn-Munkres算法来加快节点间对应关系的搜索。3-D空间中对象之间的形状相似度和欧式距离被统一利用，以通过图匹配来测量图像相似度。此外，已对提出的方法进行了分析，并与几种数据集以及两个室内真实场景中的最新算法进行了比较，