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The swift development of autonomous vehicles raises the necessity of semantically mapping the environment by producing distinguishable representations to recognise similar areas. To this end, in this article, we present an efficient technique to cut up a robot’s trajectory into semantically consistent communities based on graph-inspired descriptors. This allows an agent to localise itself in future tasks under different environmental circumstances in an urban area. The proposed semantic grouping technique utilizes the Leiden Community Detection Algorithm (LeCDA), which is a novel and efficient method of low computational complexity and exploits semantic and topometric information from the observed scenes. The presented experimentation was carried out on a novel dataset from the city of Xanthi, Greece (dubbed as Gryphonurban urban dataset), which was recorded by RGB-D, IMU and GNSS sensors mounted on a moving vehicle. Our results exhibit the formulation of a semantic map with visually coherent communities and the realisation of an effective localisation mechanism for autonomous vehicles in urban environments.

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.