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

arXiv 1705.02550 ↗First posted 7 May 2017cs.ROcited by 17

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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.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 May 2017.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 1; 2021: 7; 2022: 2; 2023: 1; 2024: 1; 2025: 3; 2026: 1.

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  1. 2021Deep Federated Learning for Autonomous Drivingcited by 54
  2. 2022Aerial Vision-and-Dialog Navigationcited by 48
  3. 2022BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computingcited by 28
  4. 2021Distribution-Aware Testing of Neural Networks Using Generative Modelscited by 22
  5. 2025See, Point, Fly: A Learning-Free VLM Framework for Universal Unmanned Aerial Navigationcited by 18
  6. 2025Aerial Vision-and-Language Navigation with Grid-based View Selection and Map Constructioncited by 12
  7. 2018OIL: Observational Imitation Learningcited by 11
  8. 2021A Vision Based Deep Reinforcement Learning Algorithm for UAV Obstacle Avoidancecited by 8
  9. 2021Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networkscited by 7
  10. 2020Learn by Observation: Imitation Learning for Drone Patrolling from Videos of A Human Navigatorcited by 5
  11. 2023A3D: Adaptive, Accurate, and Autonomous Navigation for Edge-Assisted Dronescited by 2
  12. 2021Trajectory-Constrained Deep Latent Visual Attention for Improved Local Planning in Presence of Heterogeneous Terraincited by 1
5 more
  1. 2024Angle Robustness Unmanned Aerial Vehicle Navigation in GNSS-Denied Scenarioscited by 1
  2. 2021AutoPilot: Automating SoC Design Space Exploration for SWaP Constrained Autonomous UAVscited by 0
  3. 2021Brain-Inspired Deep Imitation Learning for Autonomous Driving Systemscited by 0
  4. 2025Self-Supervised Learning to Fly using Efficient Semantic Segmentation and Metric Depth Estimation for Low-Cost Autonomous UAVscited by 0
  5. 2026A 360-Degree Vision Dataset for Learning Yaw Control on GPS-Denied Micro-UAVs in Disaster-Response-Relevant Environmentscited by 0