Exploration is an important step in autonomous navigation of robotic systems. In this paper we introduce a series of enhancements for exploration algorithms in order to use them with vision-based simultaneous localization and mapping (vSLAM) methods. We evaluate developed approaches in photo-realistic simulator in two modes: with ground-truth depths and neural network reconstructed depth maps as vSLAM input. We evaluate standard metrics in order to estimate exploration coverage.
PDF at arXiv.org: https://arxiv.org/pdf/2110.09156
Microsoft Academic: https://academic.microsoft.com/paper/3203516484/
Semantic Scholar: https://api.semanticscholar.org/CorpusID:238417375
Muravyev K., Bokovoy A., Yakovlev K. (2021) Enhancing Exploration Algorithms for Navigation with Visual SLAM. In: Kovalev S.M., Kuznetsov S.O., Panov A.I. (eds) Artificial Intelligence. RCAI 2021. Lecture Notes in Computer Science, vol 12948. Springer, Cham. Pages 197-212