Multi-layer map: Augmenting semantic visual memory

The modern view of things in the science of robotics imposes that when working in a human environment, understanding of its equivalent semantics is required. In this paper, we present a graph-based unsupervised semantic clustering method and a novel cluster matching technique, with a view to create a multi-layer semantic memory map robust to illumination changes. Using indoor data collected by an unmanned aerial robot (UAR) and a publicly available dataset, we apply a community detection algorithm (CDA) to find efficiently coherent visual data throughout the trajectory creating a semantic base map. Then, we optimize the formed communities using metric information by implementing an hierarchical agglomerative clustering algorithm. The multilayer semantic map is created by constructing map instances for variant lighting conditions and matching the generated clusters to their base map correspondence. The proposed matching method relies on the graphs centrality indicators to identify central images of a region and utilize them to efficiently extract resemblances within the base map.