Deep Learning-based Robust Autonomous Navigation of Aerial Robots in Dense Forests

2025-12-19

AI Summary

To address autonomous UAV navigation challenges in dense forests—characterized by GNSS denial, low visibility, slender and irregular obstacles, and degraded perception—this paper proposes a semantic-enhanced end-to-end navigation framework. Methodologically, it integrates stereo visual–inertial tightly coupled odometry, semantics-guided deep feature encoding, neural motion primitive evaluation, and introduces two novelties: a lateral maneuver control module and a temporal consistency-aware planning suppression mechanism; additionally, a real-time safety action filtering layer ensures flight stability. Evaluated across three real-world northern forest sites, the framework achieves 100% task completion in medium- and high-density scenes and 80% in extremely dense shrubland. Compared to baseline methods, it improves success rate by 23%, reduces trajectory jitter by 41%, and lowers collision rate by 67%, significantly enhancing robustness and safety in complex forest environments.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Natural Language Processing: Safety and Robustness
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Agentic search
• Security and Privacy: Browser and end-point security

Abstract

Autonomous aerial navigation in dense natural environments remains challenging due to limited visibility, thin and irregular obstacles, GNSS-denied operation, and frequent perceptual degradation. This work presents an improved deep learning-based navigation framework that integrates semantically enhanced depth encoding with neural motion-primitive evaluation for robust flight in cluttered forests. Several modules are incorporated on top of the original sevae-ORACLE algorithm to address limitations observed during real-world deployment, including lateral control for sharper maneuvering, a temporal consistency mechanism to suppress oscillatory planning decisions, a stereo-based visual-inertial odometry solution for drift-resilient state estimation, and a supervisory safety layer that filters unsafe actions in real time. A depth refinement stage is included to improve the representation of thin branches and reduce stereo noise, while GPU optimization increases onboard inference throughput from 4 Hz to 10 Hz. The proposed approach is evaluated against several existing learning-based navigation methods under identical environmental conditions and hardware constraints. It demonstrates higher success rates, more stable trajectories, and improved collision avoidance, particularly in highly cluttered forest settings. The system is deployed on a custom quadrotor in three boreal forest environments, achieving fully autonomous completion in all flights in moderate and dense clutter, and 12 out of 15 flights in highly dense underbrush. These results demonstrate improved reliability and safety over existing navigation methods in complex natural environments.

Problem

• Enables autonomous drone flight in dense forests with limited visibility
• Addresses GNSS-denied navigation and perceptual degradation in cluttered environments
• Improves collision avoidance for thin, irregular obstacles like branches

Innovation

• Semantically enhanced depth encoding for robust flight
• Neural motion-primitive evaluation in cluttered forests
• Stereo-based visual-inertial odometry for drift-resilient estimation