A major challenge in place recognition is to be robust against viewpoint changes and appearance changes caused by self and environmental variations. Humans achieve this by recognizing objects and their relationships in the scene under different conditions. Inspired by this, we propose a hierarchical visual place recognition pipeline based on semantic-aggregation and scene understanding for the images. The pipeline contains coarse matching and fine matching. Semantic-aggregation happens in residual aggregation of visual information and semantic information in coarse matching, and semantic association of semantic edges in fine matching. Through the above two processes, we realized a robust coarse-to-fine pipeline of visual place recognition across viewpoint and condition variations. Experimental results on the benchmark datasets show that our method performs better than several state-of-the-art methods, improving the robustness against severe viewpoint changes and appearance changes while maintaining good matching-time performance. Moreover, we prove that it is possible for a computer to realize place recognition based on scene understanding.
A Holistic Visual Place Recognition Approach Using Lightweight CNNs for Significant ViewPoint and Appearance Changes
