用灰度图补足损坏深度信息，让机器人在恶劣环境下仍能导航
Cross-Modal Reinforcement Learning for Navigation with Degraded Depth Measurements
- 通过跨模态一致性学习，从灰度图推断缺失的深度特征
- 在深度数据严重退化时仍保持导航成功率，真实环境迁移成功
- 适合做视觉导航的机器人系统，尤其在光照差或反光场景
本文提出一种跨模态学习框架，利用深度图与灰度图像之间的互补信息实现鲁棒导航。引入跨模态Wasserstein自编码器，通过强制跨模态一致性学习共享潜在表示，使系统在深度测量受污染时，仍能从灰度观测中推断出深度相关特征。所学表示与基于强化学习的策略融合，在深度传感器因光照不良或反射表面导致退化时，实现非结构化环境中的无碰撞导航。仿真与真实世界实验表明，该方法在深度严重退化下仍保持鲁棒性能，并成功迁移到真实环境。
原文摘要 · Abstract (English)
This paper presents a cross-modal learning framework that exploits complementary information from depth and grayscale images for robust navigation. We introduce a Cross-Modal Wasserstein Autoencoder that learns shared latent representations by enforcing cross-modal consistency, enabling the system to infer depth-relevant features from grayscale observations when depth measurements are corrupted. The learned representations are integrated with a Reinforcement Learning-based policy for collision-free navigation in unstructured environments when depth sensors experience degradation due to adverse conditions such as poor lighting or reflective surfaces. Simulation and real-world experiments demonstrate that our approach maintains robust performance under significant depth degradation and successfully transfers to real environments.
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