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The robustness of visual place recognition determines the accuracy of the SLAM to construct the environmental map. However, when the robot moves in the outdoor environment for a long time, it must face the challenge of drastic illumination changes (time shift, season or rain and fog weather factors), which leads to the robot’s ability to identify places is greatly restricted. This paper proposes a method for visual place recognition that is more robust to severe illumination changes. First, a generative adversarial network is introduced into visual SLAM to enhance the quality of candidate keyframes, and consistency of geographic locations corresponding to images before and after quality enhancement is evaluated, and then the image descriptor with illumination invariance is established for the robot's new observation image and keyframes of the map. Finally, the performance of the method in this paper is tested on the public dataset. The experimental results show that this method is conducive to improving the quality of environmental map nodes, and enables the robot to show a highly robust ability for visual place recognition in the face of severe illumination changes, which provides a powerful tool for robot to build an accurate environmental map.

Robust Visual Place Recognition Method for Robot Facing Drastic Illumination Changes