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.
@article{arxiv.2110.09156,
title = {Enhancing exploration algorithms for navigation with visual SLAM},
author = {Kirill Muravyev and Andrey Bokovoy and Konstantin Yakovlev},
journal= {arXiv preprint arXiv:2110.09156},
year = {2021}
}
Comments
Camera-ready version as submitted to RNCAI 2021 conference