ICUAS 2020 Paper Abstract

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Papapetros, Ioannis Tsampikos (Democritus University of Thrace), Balaska, Vasiliki (Democritus University of Thrace), Gasteratos, Antonios (Democritus University of Thrace)

Multi-Layer Map: Augmenting Semantic Visual Memory

Scheduled for presentation during the Regular Session "Navigation" (FrA1), Friday, September 4, 2020, 10:00−10:20, Macedonia Hall

2020 International Conference on Unmanned Aircraft Systems (ICUAS), September 1-4, 2020 (Postponed from June 9-12, 2020), Athens, Greece

This information is tentative and subject to change. Compiled on March 29, 2024

Keywords Navigation, Manned/Unmanned Aviation, Autonomy

Abstract

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 multi-layer 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.

 

 

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