论文标题
具有长期视觉位置识别的汇总语义骨骼表示的新型图像描述符
A Novel Image Descriptor with Aggregated Semantic Skeleton Representation for Long-term Visual Place Recognition
论文作者
论文摘要
在同时本地化和映射（SLAM）系统中，环闭合可以消除累积错误，这是通过Visual Place识别（VPR）完成的，该任务是通过匹配特定的表面描述符来从一组预存储的顺序图像中检索当前场景的任务。在城市场景中，季节和照明引起的外观变化给场景描述符的稳健性带来了巨大的挑战。语义分割图像不仅可以传递对象的形状信息，还可以传递其类别和空间关系，而这些信息不会受到场景外观变化的影响。在本文中，由局部汇总描述符（VLAD）的向量创新，我们提出了一个新型的图像描述符，该描述符具有汇总的语义骨骼表示（SSR），称为SSR-VLAD，用于在环境的急剧外观下进行VPR。一个图像的SSR-VLAD汇总了每个类别的语义骨架特征，并编码图像语义信息的时空分布信息。我们在三个挑战性城市场景的公共数据集上进行了一系列实验。与四种最先进的VPR方法 - COHOG，NETVLAD，LOST-X和区域VLAD相比，VPR与SSR-VLAD相匹配以优于这些方法，并同时保持竞争性的实时性能。
In a Simultaneous Localization and Mapping (SLAM) system, a loop-closure can eliminate accumulated errors, which is accomplished by Visual Place Recognition (VPR), a task that retrieves the current scene from a set of pre-stored sequential images through matching specific scene-descriptors. In urban scenes, the appearance variation caused by seasons and illumination has brought great challenges to the robustness of scene descriptors. Semantic segmentation images can not only deliver the shape information of objects but also their categories and spatial relations that will not be affected by the appearance variation of the scene. Innovated by the Vector of Locally Aggregated Descriptor (VLAD), in this paper, we propose a novel image descriptor with aggregated semantic skeleton representation (SSR), dubbed SSR-VLAD, for the VPR under drastic appearance-variation of environments. The SSR-VLAD of one image aggregates the semantic skeleton features of each category and encodes the spatial-temporal distribution information of the image semantic information. We conduct a series of experiments on three public datasets of challenging urban scenes. Compared with four state-of-the-art VPR methods- CoHOG, NetVLAD, LOST-X, and Region-VLAD, VPR by matching SSR-VLAD outperforms those methods and maintains competitive real-time performance at the same time.
