  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.
