# Deep Convolutional Neural Network-Based Autonomous Drone Navigation

arXiv: https://arxiv.org/abs/1905.01657 · HTML: https://mlsanity.com/paper/1905.01657/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2019.

## Built on by (most cited first, 4 of 4)

- [NaviSlim: Adaptive Context-Aware Navigation and Sensing via Dynamic Slimmable Networks](https://mlsanity.com/paper/2407.01563/) (2024)
- [Towards Task-Oriented Flying: Framework, Infrastructure, and Principles](https://mlsanity.com/paper/2504.15129/) (2025)
- [Renderers are Good Zero-Shot Representation Learners: Exploring Diffusion Latents for Metric Learning](https://mlsanity.com/paper/2306.10721/) (2023)
- [Neural Network Verification for Gliding Drone Control: A Case Study](https://mlsanity.com/paper/2505.00622/) (2025)

## Abstract (arXiv)

This paper presents a novel approach for aerial drone autonomous navigation along predetermined paths using only visual input form an onboard camera and without reliance on a Global Positioning System (GPS). It is based on using a deep Convolutional Neural Network (CNN) combined with a regressor to output the drone steering commands. Furthermore, multiple auxiliary navigation paths that form a navigation envelope are used for data augmentation to make the system adaptable to real-life deployment scenarios. The approach is suitable for automating drone navigation in applications that exhibit regular trips or visits to same locations such as environmental and desertification monitoring, parcel/aid delivery and drone-based wireless internet delivery. In this case, the proposed algorithm replaces human operators, enhances accuracy of GPS-based map navigation, alleviates problems related to GPS-spoofing and enables navigation in GPS-denied environments. Our system is tested in two scenarios using the Unreal Engine-based AirSim plugin for drone simulation with promising results of average cross track distance less than 1.4 meters and mean waypoints minimum distance of less than 1 meter.