Enhancing exploration algorithms for navigation with visual SLAM
arXiv:2110.09156 · doi:10.1007/978-3-030-86855-0_14
Abstract
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.
Camera-ready version as submitted to RNCAI 2021 conference