Visual Place Recognition Method Based on Attention Mechanism - DAI Tian-hong; YANG Xiao-yun; SONG Jie-qi (Journal of Harbin University of Science and Technology)
Aiming at the problem of poor accuracy and robustness of the existing visual place recognition methods when the image appearance changes and the viewing angle changes, a visual place recognition method combined with the attention mechanism is proposed. Firstly, we use the convolutional neural network HybridNet pre-trained on a large location dataset to extract features.Then, we use the context attention mechanism to assign weight values to different regions of the image to construct an attention mask based on multi-layer convolution features. Finally, we combine the mask with the convolution feature to construct the image feature descriptor fused with the attention mechanism so as to improve the robustness of the feature. Testing experiments on two typical place recognition datasets show that the method combined with the attention mechanism can effectively distinguish between the regions related to place recognition and the unrelated regions in the image, and it can improve the accuracy and robustness of recognition in scenes with changes in appearance and viewpoints.