Hybrid CNN-Transformer Features for Visual Place Recognition

Visual place recognition is a challenging problem in robotics and autonomous systems because the scene undergoes appearance and viewpoint changes in a changing world. Existing state-of-the-art methods heavily rely on CNN-based architectures. However, CNN cannot effectively model image spatial structure information due to the inherent locality. To address this issue, this paper proposes a novel Transformer-based place recognition method to combine local details, spatial context, and semantic information for image feature embedding. Firstly, to overcome the inherent locality of the convolutional neural network (CNN), a hybrid CNN-Transformer feature extraction network is introduced. The network utilizes the feature pyramid based on CNN to obtain the detailed visual understanding, while using the vision Transformer to model image contextual information and aggregate task-related features dynamically. Specifically, the multi-level output tokens from the Transformer are fed into a single Transformer encoder block to fuse multi-scale spatial information. Secondly, to acquire the multi-scale semantic information, a global semantic NetVLAD aggregation strategy is constructed. This strategy employs semantic enhanced NetVLAD, imposing prior knowledge on the terms of the Vector of Locally Aggregated Descriptors (VLAD), to aggregate multi-level token maps, and further concatenates the multi-level semantic features globally. Finally, to alleviate the disadvantage that the fixed margin of triplet loss leads to the suboptimal convergence, an adaptive triplet loss with dynamic margin is proposed. Extensive experiments on public datasets show that the learned features are robust to appearance and viewpoint changes and achieve promising performance compared to state-of-the-arts.