# Deep Neural Network for Real-Time Autonomous Indoor Navigation

arXiv: https://arxiv.org/abs/1511.04668 · HTML: https://mlsanity.com/paper/1511.04668/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2015.

## Built on by (most cited first, 9 of 9)

- [DepthLab: From Partial to Complete](https://mlsanity.com/paper/2412.18153/) (2024)
- [NavRL: Learning Safe Flight in Dynamic Environments](https://mlsanity.com/paper/2409.15634/) (2024)
- [Language Understanding for Field and Service Robots in a Priori Unknown Environments](https://mlsanity.com/paper/2105.10396/) (2021)
- [Learn by Observation: Imitation Learning for Drone Patrolling from Videos of A Human Navigator](https://mlsanity.com/paper/2008.13193/) (2020)
- [VGF-Net: Visual-Geometric Fusion Learning for Simultaneous Drone Navigation and Height Mapping](https://mlsanity.com/paper/2104.03109/) (2021)
- [Smooth Trajectory Collision Avoidance through Deep Reinforcement Learning](https://mlsanity.com/paper/2210.06377/) (2022)
- [Angle Robustness Unmanned Aerial Vehicle Navigation in GNSS-Denied Scenarios](https://mlsanity.com/paper/2402.02405/) (2024)
- [NavRL++: A System-Level Framework for Improving Sim-to-Real Transfer in Reinforcement Learning-Based Robot Navigation](https://mlsanity.com/paper/2605.15559/) (2026)
- [A Multiple Classifier Approach for Concatenate-Designed Neural Networks](https://mlsanity.com/paper/2101.05457/) (2021)

## Abstract (arXiv)

Autonomous indoor navigation of Micro Aerial Vehicles (MAVs) possesses many challenges. One main reason is that GPS has limited precision in indoor environments. The additional fact that MAVs are not able to carry heavy weight or power consuming sensors, such as range finders, makes indoor autonomous navigation a challenging task. In this paper, we propose a practical system in which a quadcopter autonomously navigates indoors and finds a specific target, i.e., a book bag, by using a single camera. A deep learning model, Convolutional Neural Network (ConvNet), is used to learn a controller strategy that mimics an expert pilot's choice of action. We show our system's performance through real-time experiments in diverse indoor locations. To understand more about our trained network, we use several visualization techniques.