Abstract
In the nuclear industry, the need for improved reliability in current and future technology hinders the deployment of autonomous robotic systems. The following research aims to develop a method of reliably mapping a large environment and abstracting the map into a sparse node graph to create a more efficient data form. The proposed data form allows for efficient storage whilst maintaining important map features and coverage. The method utilises an expanding node algorithm to convert standard occupancy maps to a sparse node graph representation. The algorithm’s effectiveness has been tested on simulated maps and real-world maps to test the compression factor for a wide range of scenarios. The algorithm is expanded to function on a semi-unknown map abstracting during exploration.
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Batty, D., Manes, L., West, A., Patel, M., Caliskanelli, I., Paoletti, P. (2023). Occupancy Map Abstraction for Higher Level Mission Planning of Autonomous Robotic Exploration in Hazardous Nuclear Environments. In: Iida, F., Maiolino, P., Abdulali, A., Wang, M. (eds) Towards Autonomous Robotic Systems. TAROS 2023. Lecture Notes in Computer Science(), vol 14136. Springer, Cham. https://doi.org/10.1007/978-3-031-43360-3_7
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