Loop closure detection is a key technology for robotic navigation. Existing research primarily focuses on feature extraction from global scenes but often neglects local overhead occlusion scenes. In these local scenes, objects such as vehicles, trees, and buildings vary in height, creating a complex multi-layered structure with vertical occlusions. Current methods predominantly employ a single-level extraction strategy to construct descriptors, which fails to capture the characteristics of occluded objects. This limitation results in descriptors with restricted descriptive capabilities. This paper introduces a descriptor named Hierarchy Scan Context (HSC) to address this shortfall. HSC effectively extracts height feature information of objects at different levels in overhead occlusion scenes through hierarchical division, demonstrating enhanced descriptive capabilities. Additionally, a time series enhancement strategy is proposed to reduce the number of algorithmic missed detections. In the experiments, the proposed method is validated using a self-collected dataset and the public KITTI and NCLT datasets, demonstrating superior performance compared to competitive methods. Furthermore, the proposed method also achieves an average maximum F1 score of 0.92 in experiments conducted on nine selected road segments with overhead occlusion.

闭环检测是机器人导航的关键技术。现有的研究主要集中在全局场景的特征提取上，但往往忽略了局部头顶遮挡场景。在这些局部场景中，车辆、树木和建筑物等物体的高度各不相同，形成具有垂直遮挡的复杂多层结构。当前的方法主要采用单级提取策略来构造描述符，这无法捕获被遮挡对象的特征。这种限制导致描述符的描述能力受到限制。本文引入了一种名为层次扫描上下文（HSC）的描述符来解决这一缺陷。 HSC通过层次划分，有效提取了头顶遮挡场景中不同层次物体的高度特征信息，展现出增强的描述能力。此外，还提出了时间序列增强策略来减少算法漏检的数量。在实验中，使用自行收集的数据集以及公共 KITTI 和 NCLT 数据集对所提出的方法进行了验证，证明了与竞争方法相比的优越性能。此外，在九个选定的高架遮挡路段上进行的实验中，所提出的方法还实现了 0.92 的平均最大 F1 分数。