Abstract
Deploying autonomous robots capable of exploring unknown environments has long been a topic of great relevance to the robotics community. In this work, we take a further step in that direction by presenting an open-source active visual SLAM framework that leverages the accuracy of a state-of-the-art graph-SLAM system and takes advantage of the fast utility computation that exploiting the structure of the underlying pose-graph offers. We achieve fast decision making through careful estimation of a posteriori weighted pose-graphs and by employing a utility function that balances exploration and exploitation principles.
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Notes
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Throughout this paper, we will equally use FIM and Hessian matrix, since they are equivalent when evaluating the latter at the maximum likelihood estimate.
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Acknowledgments
This work was supported by the Spanish government under grant PID2019-108398GB-I00 and by Aragón government under grant T45-20R.
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Placed, J.A., Rodríguez, J.J.G., Tardós, J.D., Castellanos, J.A. (2023). ExplORB-SLAM: Active Visual SLAM Exploiting the Pose-graph Topology. In: Tardioli, D., Matellán, V., Heredia, G., Silva, M.F., Marques, L. (eds) ROBOT2022: Fifth Iberian Robotics Conference. ROBOT 2022. Lecture Notes in Networks and Systems, vol 589. Springer, Cham. https://doi.org/10.1007/978-3-031-21065-5_17
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