Abstract
-Navigation through narrow and irregular gaps is an essential skill in autonomous drones for applications such as inspection, search-and-rescue, and disaster response. However, traditional planning and control methods rely on explicit gap extraction and measurement, while recent end-to-end approaches often assume regularly shaped gaps, leading to poor generalization and limited practicality. In this work, we present a fully vision-based, end-to-end framework that maps depth images directly to control commands, enabling drones to traverse complex gaps within unseen environments. Operating in the Special Euclidean group SE(3), where position and orientation are tightly coupled, the framework leverages differentiable simulation, a Stop-Gradient operator, and a Bimodal Initialization Distribution to achieve stable traversal through consecutive gaps. Two auxiliary prediction modules-a gap-crossing success classifier and a traversability predictor-further enhance continuous navigation and safety. Extensive simulation and real-world experiments demonstrate the approach's effectiveness, generalization capability, and practical robustness.
Abstract (translated)
在自主无人机中，穿越狭窄且不规则间隙是一项关键技能，常用于检测、搜救和灾害响应等任务。然而，传统规划与控制方法依赖显式的间隙提取与测量，而近年端到端方法常假设间隙形状规则，导致泛化能力差且实用性有限。本研究提出一个完全基于视觉的端到端框架，直接将深度图像映射为控制指令，使无人机能够穿越未知环境中的复杂间隙。该框架在位置与姿态紧密耦合的特殊欧几里得群SE(3)中运行，通过可微分仿真、停止梯度算子及双峰初始化分布，实现连续间隙间的稳定穿越。两个辅助预测模块——间隙穿越成功分类器与可穿越性预测器——进一步提升了连续导航与安全性。大量仿真与真实实验验证了该方法的高效性、泛化能力及实际鲁棒性。
URL
https://arxiv.org/abs/2604.02779
