# Learning to Explore Indoor Environments using Autonomous Micro Aerial Vehicles

arXiv: https://arxiv.org/abs/2309.06986 · HTML: https://mlsanity.com/paper/2309.06986/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2023.

## Built on by (most cited first, 5 of 5)

- [MapEx: Indoor Structure Exploration with Probabilistic Information Gain from Global Map Predictions](https://mlsanity.com/paper/2409.15590/) (2024)
- [Beyond the Frontier: Predicting Unseen Walls from Occupancy Grids by Learning from Floor Plans](https://mlsanity.com/paper/2406.09160/) (2024)
- [SIGN: Safety-Aware Image-Goal Navigation for Autonomous Drones via Reinforcement Learning](https://mlsanity.com/paper/2508.12394/) (2025)
- [MapExRL: Human-Inspired Indoor Exploration with Predicted Environment Context and Reinforcement Learning](https://mlsanity.com/paper/2503.01548/) (2025)
- [Beyond Frontiers: Scene-Anomaly Guided Autonomous Exploration](https://mlsanity.com/paper/2607.15828/) (2026)

## Abstract (arXiv)

In this paper, we address the challenge of exploring unknown indoor aerial environments using autonomous aerial robots with Size Weight and Power (SWaP) constraints. The SWaP constraints induce limits on mission time requiring efficiency in exploration. We present a novel exploration framework that uses Deep Learning (DL) to predict the most likely indoor map given the previous observations, and Deep Reinforcement Learning (DRL) for exploration, designed to run on modern SWaP constraints neural processors. The DL-based map predictor provides a prediction of the occupancy of the unseen environment while the DRL-based planner determines the best navigation goals that can be safely reached to provide the most information. The two modules are tightly coupled and run onboard allowing the vehicle to safely map an unknown environment. Extensive experimental and simulation results show that our approach surpasses state-of-the-art methods by 50-60% in efficiency, which we measure by the fraction of the explored space as a function of the length of the trajectory traveled.