# From Monocular SLAM to Autonomous Drone Exploration

arXiv: https://arxiv.org/abs/1609.07835 · HTML: https://mlsanity.com/paper/1609.07835/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2016.

## Built on by (most cited first, 10 of 10)

- [Learning Efficient Multi-Agent Cooperative Visual Exploration](https://mlsanity.com/paper/2110.05734/) (2021)
- [Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration](https://mlsanity.com/paper/2301.03398/) (2023)
- [ORBSLAM3-Enhanced Autonomous Toy Drones: Pioneering Indoor Exploration](https://mlsanity.com/paper/2312.13385/) (2023)
- [Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields](https://mlsanity.com/paper/2107.06028/) (2021)
- [Keeping Less is More: Point Sparsification for Visual SLAM](https://mlsanity.com/paper/2207.00225/) (2022)
- [Radiance Field Learners As UAV First-Person Viewers](https://mlsanity.com/paper/2408.05533/) (2024)
- [QueensCAMP: an RGB-D dataset for robust Visual SLAM](https://mlsanity.com/paper/2410.12520/) (2024)
- [Autonomous Aerial Robot for High-Speed Search and Intercept Applications](https://mlsanity.com/paper/2112.05465/) (2021)
- [Urban Drone Navigation: Autoencoder Learning Fusion for Aerodynamics](https://mlsanity.com/paper/2310.08830/) (2023)
- [Self-Supervised Learning to Fly using Efficient Semantic Segmentation and Metric Depth Estimation for Low-Cost Autonomous UAVs](https://mlsanity.com/paper/2510.16624/) (2025)

## Abstract (arXiv)

Micro aerial vehicles (MAVs) are strongly limited in their payload and power capacity. In order to implement autonomous navigation, algorithms are therefore desirable that use sensory equipment that is as small, low-weight, and low-power consuming as possible. In this paper, we propose a method for autonomous MAV navigation and exploration using a low-cost consumer-grade quadrocopter equipped with a monocular camera. Our vision-based navigation system builds on LSD-SLAM which estimates the MAV trajectory and a semi-dense reconstruction of the environment in real-time. Since LSD-SLAM only determines depth at high gradient pixels, texture-less areas are not directly observed so that previous exploration methods that assume dense map information cannot directly be applied. We propose an obstacle mapping and exploration approach that takes the properties of our semi-dense monocular SLAM system into account. In experiments, we demonstrate our vision-based autonomous navigation and exploration system with a Parrot Bebop MAV.