发表机构
Norwegian Institute of Science and Technology（挪威科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种具身感知导航策略，通过将任意深度传感器数据反投影到统一球面图像，实现跨相机布局的零样本泛化，成功率随覆盖提升至95%。
AI 中文摘要
现有的视觉导航策略本质上受限于固定的相机配置，这构成了跨异构机器人传感器布局进行零样本部署的根本障碍。为克服这一限制，我们提出了一种具身感知的导航策略，能够在特定空中平台上跨不同深度传感器配置进行泛化。我们的方法不是隐式学习空间对齐，而是显式地将来自任意深度传感器载荷（传感器数量、安装外参和内参各异）的深度测量反投影到共享的机器人中心坐标系，将其拼接成统一的球面距离图像和二元有效性掩码。该掩码使下游策略能够明确区分已覆盖空间与未观测盲区。通过采用激进相机随机化的强化学习训练，我们的策略能够零样本泛化到包含多达七个相机的未见布局，随着总空间感知覆盖范围的增加，成功率从78%提升至95%。最后，在物理四旋翼飞行器上进行的真实世界飞行试验，在布满障碍物的走廊和室外森林中进行，验证了该策略跨相机配置的零样本迁移能力及其对突然在线传感器丢失的鲁棒性。
英文摘要
Existing visual navigation policies are inherently bound to fixed camera configurations, creating a fundamental barrier to zero-shot deployment across heterogeneous robot sensor layouts. To overcome this limitation, we present an embodiment-informed navigation policy capable of generalizing across diverse depth sensor configurations on a specific aerial platform. Instead of implicitly learning spatial alignments, our approach explicitly unprojects depth measurements from arbitrary depth sensor payloads, varying in sensor count, mounting extrinsics, and intrinsics, into a shared robot-centric frame, stitching them into a unified spherical range image and a binary validity mask. This mask allows the downstream policy to explicitly distinguish covered space from unobserved blind spots. Trained via reinforcement learning with aggressive camera randomization, our policy generalizes zero-shot to unseen layouts featuring up to seven cameras, scaling success rates from 78% to 95% as total spatial sensing coverage increases. Finally, real-world flight trials on a physical quadrotor, conducted in an obstacle-filled corridor and an outdoor forest, validate the policy's zero-shot transfer across camera configurations and its resilience to sudden online sensor dropouts.
