A Hierarchical Utilization of Semantic Gradients and Scene Structure for Visual Place Recognition

Abstract

Visual Place Recognition (VPR) is a fundamental element for long-term Simultaneous Localization and Mapping (SLAM) systems. For long-term VPR, severe appearance and viewpoint variations are inevitable. In this paper, we introduce a novel VPR system named Semantic Scene Structure Place Recognition (3SPR), inspired by the repeatability of semantic gradients and the scene structure of urban environments. Semantic gradients are densely sampled according to the sum of absolute gradients of all channels in the logits layer. Features of the semantic gradients in different layers are concatenated to exploit features’ characteristics at different levels. Based on partitions by vanishing points of road lines and Vector of Locally Aggregated Descriptors (VLAD), the Scene Structure VLAD (SSVLAD) is generated from concatenated features of the semantic gradients. Moreover, a local point group match method is used to enhance the spatial verification. Experimental results show that our method achieves state-of-the-art performance on the Oxford Robotcar dataset and the Synthia dataset.