Abstract
This paper presents MoDSeM, a software framework for cooperative perception supporting teams of robots. MoDSeM aims to provide a flexible semantic mapping framework able to represent all spatial information perceived in missions involving teams of robots, and to formalize the development of perception software, promoting the implementation of reusable modules that can fit varied team constitutions. We provide an overview of MoDSeM, and describe how it can be applied to multi-robot systems, discussing several sub-problems such as history and memory, or centralized vs distributed perception. Aiming to demonstrate the functionality of our prototype, preliminary experiments took place in simulation, using a \(100 \times 100 \times 100\) m simulated map to demonstrate its ability to receive, store and retrieve information stored in semantic voxel grids, using ROS as a transport layer and OpenVDB as a grid storage mechanism. Results show the appropriateness of ROS and OpenVDB as a back-end for supporting the prototype, achieving a promising performance in all aspects of the task. Future developments will make use of these results to apply MoDSeM in realistic scenarios, including multi-robot indoor surveillance and precision forestry operations.
This work was supported by the Seguranças robóTicos coOPerativos (STOP, ref. CENTRO-01-0247-FEDER-017562), the Safety, Exploration and Maintenance of Forests with Ecological Robotics (SEMFIRE, ref. CENTRO-01-0247-FEDER-03269) and the Centre of Operations for Rethinking Engineering (CORE, ref. CENTRO-01-0247-FEDER-037082) research projects co-funded by the “Agência Nacional de Inovação” within the Portugal2020 programme.
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Aliasing artefacts can be seen due to the downsampling procedure that must be applied for visualization; it is not possible to represent the near-20-million voxels of the original map in ROS’s visualization tool, rviz.
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Martins, G.S., Ferreira, J.F., Portugal, D., Couceiro, M.S. (2019). MoDSeM: Towards Semantic Mapping with Distributed Robots. In: Althoefer, K., Konstantinova, J., Zhang, K. (eds) Towards Autonomous Robotic Systems. TAROS 2019. Lecture Notes in Computer Science(), vol 11650. Springer, Cham. https://doi.org/10.1007/978-3-030-25332-5_12
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