A Novel Image Descriptor with Aggregated Semantic Skeleton Representation for Long-term Visual Place Recognition
This work addresses the challenge of long-term visual place recognition in urban scenes for SLAM systems, offering an incremental improvement by leveraging semantic segmentation to enhance robustness against appearance changes.
The paper tackles the problem of robust visual place recognition under drastic appearance variations like seasons and illumination by proposing a novel image descriptor called SSR-VLAD, which aggregates semantic skeleton features and encodes spatial-temporal distribution information, achieving superior performance compared to four state-of-the-art methods on three public datasets.
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.