# Enhancing exploration algorithms for navigation with visual SLAM

arXiv: https://arxiv.org/abs/2110.09156 · HTML: https://mlsanity.com/paper/2110.09156/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2021.

## Built on by (most cited first, 2 of 2)

- [Interactive Semantic Map Representation for Skill-based Visual Object Navigation](https://mlsanity.com/paper/2311.04107/) (2023)
- [NavTopo: Leveraging Topological Maps For Autonomous Navigation Of a Mobile Robot](https://mlsanity.com/paper/2410.11492/) (2024)

## Abstract (arXiv)

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.