Semantic Boundary Constrained Network for Visual Place Recognition Under Adverse Conditions

Accurate localization of autonomous vehicles is crucial for autonomous driving and safety, especially in complex urban environments where high-precision GPS is not available. Visual place recognition (VPR) uses visual cues to identify the current location from a known database, serving as an auxiliary means for precise localization in autonomous driving. In real-world applications, variations in scene appearance due to changes in illumination and seasons present significant challenges for VPR. Current VPR methods often fail in adverse visual environments due to their inability to provide robust scene descriptions. Therefore, the extraction of stable and effective information from images is relatively important. In this paper, we propose a novel feature extraction network, termed SBCNet. This network is designed to capture semantic boundaries and texture details within images by training an auxiliary semantic boundary detection task. By focusing on these fundamental elements, the model’s perceptual capacity for structural features can be enhanced. Moreover, we introduce a semantic edge attention module that generates spatial attention maps based on semantic edges and texture details, allowing for the comprehensive utilization of pivotal structural cues. With this explicit guide, the network prioritizes local regions with appearance invariance during the feature extraction process. Experimental results demonstrate that our method maintains robust performance under various adverse visual conditions. Even in low-light environments, such as those encountered at night, our method exhibits commendable performance.