Visual SLAM approaches typically depend on loop closure detection to correct the inconsistencies that may arise during the map and camera trajectory calculations, typically making use of point features for detecting and closing the existing loops. In low-textured scenarios, however, it is difficult to find enough point features and, hence, the performance of these solutions drops drastically. An alternative for human-made scenarios, due to their structural regularity, is the use of geometrical cues such as straight segments, frequently present within these environments. Under this context, in this paper we introduce LiPo-LCD, a novel appearance-based loop closure detection method that integrates lines and points. Adopting the idea of incremental Bag-of-Binary-Words schemes, we build separate BoW models for each feature, and use them to retrieve previously seen images using a late fusion strategy. Additionally, a simple but effective mechanism, based on the concept of island, groups similar images close in time to reduce the image candidate search effort. A final step validates geometrically the loop candidates by incorporating the detected lines by means of a process comprising a line feature matching stage, followed by a robust spatial verification stage, now combining both lines and points. As it is reported in the paper, LiPo-LCD compares well with several state-of-the-art solutions for a number of datasets involving different environmental conditions.

中文翻译：

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LiPo-LCD：组合线和点以进行基于外观的环路闭合检测

视觉 SLAM 方法通常依赖回环检测来纠正在地图和相机轨迹计算过程中可能出现的不一致性，通常使用点特征来检测和关闭现有的回环。然而，在低纹理场景中，很难找到足够的点特征，因此这些解决方案的性能急剧下降。由于其结构规律性，人造场景的替代方案是使用几何线索，例如在这些环境中经常出现的直线段。在这种背景下，在本文中，我们介绍了 LiPo-LCD，这是一种新颖的基于外观的闭环检测方法，它集成了线和点。采用增量Bag-of-Binary-Words方案的思想，我们为每个特征构建单独的BoW模型，并使用它们使用后期融合策略检索以前看到的图像。此外，一种简单但有效的机制，基于岛的概念，将相似的图像及时分组，以减少图像候选搜索的工作量。最后一步通过包括线特征匹配阶段和稳健空间验证阶段（现在结合线和点）的过程合并检测到的线来几何验证循环候选者。正如论文中所报道的那样，对于涉及不同环境条件的许多数据集，LiPo-LCD 与几种最先进的解决方案进行了很好的比较。最后一步通过包括线特征匹配阶段和稳健空间验证阶段（现在结合线和点）的过程合并检测到的线来几何验证循环候选者。正如论文中所报道的那样，对于涉及不同环境条件的许多数据集，LiPo-LCD 与几种最先进的解决方案进行了很好的比较。最后一步通过包括线特征匹配阶段和稳健空间验证阶段（现在结合线和点）的过程合并检测到的线来几何验证循环候选者。正如论文中所报道的那样，对于涉及不同环境条件的许多数据集，LiPo-LCD 与几种最先进的解决方案进行了很好的比较。