# Toward Low-Flying Autonomous MAV Trail Navigation using Deep Neural Networks for Environmental Awareness

arXiv: https://arxiv.org/abs/1705.02550 · HTML: https://mlsanity.com/paper/1705.02550/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2017.

## Built on by (most cited first, 19 of 19)

- [Deep Federated Learning for Autonomous Driving](https://mlsanity.com/paper/2110.05754/) (2021)
- [Aerial Vision-and-Dialog Navigation](https://mlsanity.com/paper/2205.12219/) (2022)
- [BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing](https://mlsanity.com/paper/2201.02693/) (2022)
- [NavRL: Learning Safe Flight in Dynamic Environments](https://mlsanity.com/paper/2409.15634/) (2024)
- [Distribution-Aware Testing of Neural Networks Using Generative Models](https://mlsanity.com/paper/2102.13602/) (2021)
- [See, Point, Fly: A Learning-Free VLM Framework for Universal Unmanned Aerial Navigation](https://mlsanity.com/paper/2509.22653/) (2025)
- [Aerial Vision-and-Language Navigation with Grid-based View Selection and Map Construction](https://mlsanity.com/paper/2503.11091/) (2025)
- [OIL: Observational Imitation Learning](https://mlsanity.com/paper/1803.01129/) (2018)
- [A Vision Based Deep Reinforcement Learning Algorithm for UAV Obstacle Avoidance](https://mlsanity.com/paper/2103.06403/) (2021)
- [Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks](https://mlsanity.com/paper/2106.13358/) (2021)
- [Learn by Observation: Imitation Learning for Drone Patrolling from Videos of A Human Navigator](https://mlsanity.com/paper/2008.13193/) (2020)
- [A3D: Adaptive, Accurate, and Autonomous Navigation for Edge-Assisted Drones](https://mlsanity.com/paper/2307.09880/) (2023)
- [Trajectory-Constrained Deep Latent Visual Attention for Improved Local Planning in Presence of Heterogeneous Terrain](https://mlsanity.com/paper/2112.04684/) (2021)
- [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)
- [AutoPilot: Automating SoC Design Space Exploration for SWaP Constrained Autonomous UAVs](https://mlsanity.com/paper/2102.02988/) (2021)
- [Brain-Inspired Deep Imitation Learning for Autonomous Driving Systems](https://mlsanity.com/paper/2107.14654/) (2021)
- [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)
- [A 360-Degree Vision Dataset for Learning Yaw Control on GPS-Denied Micro-UAVs in Disaster-Response-Relevant Environments](https://mlsanity.com/paper/2608.19866/) (2026)

## Abstract (arXiv)

We present a micro aerial vehicle (MAV) system, built with inexpensive off-the-shelf hardware, for autonomously following trails in unstructured, outdoor environments such as forests. The system introduces a deep neural network (DNN) called TrailNet for estimating the view orientation and lateral offset of the MAV with respect to the trail center. The DNN-based controller achieves stable flight without oscillations by avoiding overconfident behavior through a loss function that includes both label smoothing and entropy reward. In addition to the TrailNet DNN, the system also utilizes vision modules for environmental awareness, including another DNN for object detection and a visual odometry component for estimating depth for the purpose of low-level obstacle detection. All vision systems run in real time on board the MAV via a Jetson TX1. We provide details on the hardware and software used, as well as implementation details. We present experiments showing the ability of our system to navigate forest trails more robustly than previous techniques, including autonomous flights of 1 km.