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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Towards Evolving Cooperative Mapping for Large-Scale UAV Teams
AU - Shafipour Yourdshahi, Elnaz
AU - Angelov, Plamen Parvanov
AU - Soriano Marcolino, Leandro
AU - Tsianakas, Georgios
N1 - ©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2019/1/31
Y1 - 2019/1/31
N2 - A team of UAVs has great potential to handle real-world challenges. Knowing the environment is essential to perform in an effective manner. However, in many situations, a map of the environment will not be available. Additionally, for autonomous systems, it is necessary to have approaches that require little energy, computing, power, weight and size. To address this, we propose a light-weight, evolving, and memory efficient cooperative approach for estimating the map of an environment with a team of UAVs. Additionally, we present proof-of-concept experiments with real-life flights, showing that we can estimate maps using an off-the-shelf web-camera.
AB - A team of UAVs has great potential to handle real-world challenges. Knowing the environment is essential to perform in an effective manner. However, in many situations, a map of the environment will not be available. Additionally, for autonomous systems, it is necessary to have approaches that require little energy, computing, power, weight and size. To address this, we propose a light-weight, evolving, and memory efficient cooperative approach for estimating the map of an environment with a team of UAVs. Additionally, we present proof-of-concept experiments with real-life flights, showing that we can estimate maps using an off-the-shelf web-camera.
U2 - 10.1109/SSCI.2018.8628838
DO - 10.1109/SSCI.2018.8628838
M3 - Conference contribution/Paper
SP - 2262
EP - 2269
BT - 2018 IEEE Symposium Series on Computational Intelligence (SSCI)
PB - IEEE
ER -