外观
论文arXiv 2512.175531 个月前
基于深度学习的空中机器人在茂密森林中的鲁棒自主导航
原文链接：Deep learning-based robust autonomous navigation of aerial robots in dense forests（arXiv 2512.17553 · 发布日期：2026-09-03）
Deep learning-based robust autonomous navigation of aerial robots in dense forests
针对密林中空中机器人自主导航，提出融合语义深度编码与神经运动基元评估的框架，并在三处北方森林用定制四旋翼验证。
核心要点
- 密集自然环境存在视野受限、细小不规则障碍、无GNSS和感知退化等挑战。
- 在sevae-ORACLE基础上加入横向控制、时序一致性、立体VIO和实时安全监督层。
- 深度细化改善细枝表示并降低立体噪声，GPU优化将机载推理从4 Hz提升至10 Hz。
- 定制四旋翼在三处北方森林部署，中等和密集场景15/15完成，高密灌丛12/15。
验证范围与局限：验证限于定制四旋翼在三个北方森林环境中的实飞任务。
英文摘要 (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 were 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 VIO solution for drift-resilient state estimation, and a supervisory safety layer that filters unsafe actions in real time. A depth-refinement stage was additionally included to improve the representation of thin branches and reduce stereo noise, while GPU optimization increased onboard inference throughput from 4 Hz to 10 Hz. To contextualize system performance, a comparative evaluation was conducted against several existing learning-based navigation methods tested under identical environmental conditions and hardware constraints. The proposed approach demonstrated consistently higher success rates, more stable trajectories, and improved collision avoidance, particularly in highly cluttered forest settings. The system was deployed on a custom quadrotor in three boreal forest environments. In moderate-clutter and dense environments, it achieved fully autonomous completion in all 15/15 flights, while in a highly dense underbrush setting it succeeded in 12/15 trials, representing a substantial improvement over the previous baseline, which completed only 5/15 comparable missions. These findings demonstrate that the proposed system provides a measurable improvement in reliability and safety over existing navigation methods in complex natural environments.
作者：Guglielmo Del Col, Väinö Karjalainen, Teemu Hakala, Yibo Zhang, Eija Honkavaara
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