基于RGB影像的光谱签名映射用于地形感知导航
Spectral Signature Mapping from RGB Imagery for Terrain-Aware Navigation
AI总结：
本文提出RS-Net，通过RGB影像预测光谱签名，用于机器人地形感知导航和摩擦系数估计。
摘要：
在户外环境中实现成功导航需要准确预测机器人与地形之间的物理相互作用。许多先前的方法依赖于几何或语义标签来分类可通行的表面。然而，此类标签无法区分在视觉上相似但材料属性不同的表面。光谱传感器能够从多个波长带测量的表面反射率推断出材料组成。尽管光谱感知在机器人学中日益受到关注，但广泛应用仍受到需要定制硬件集成、高传感器成本和计算密集型处理管道的限制。在本文中，我们提出了RGB图像到光谱签名神经网络（RS-Net），这是一种设计用于弥合RGB传感可及性与光谱数据提供的丰富材料信息之间差距的深度神经网络。RS-Net从RGB块预测光谱签名，我们将这些映射到地形标签和摩擦系数。所得到的地形分类被整合到基于采样的运动规划器中，用于在户外环境中运行的轮式机器人。同样，摩擦估计被整合到基于接触力的MPC中，用于在滑腻表面上导航的四足机器人。总体而言，我们的框架在训练期间学习任务相关的物理属性，之后仅依赖RGB传感进行运行时处理。
英文摘要：
Successful navigation in outdoor environments requires accurate prediction of the physical interactions between the robot and the terrain. Many prior methods rely on geometric or semantic labels to classify traversable surfaces. However, such labels cannot distinguish visually similar surfaces that differ in material properties. Spectral sensors enable inference of material composition from surface reflectance measured across multiple wavelength bands. Although spectral sensing is gaining traction in robotics, widespread deployment remains constrained by the need for custom hardware integration, high sensor costs, and compute-intensive processing pipelines. In this paper, we present the RGB Image to Spectral Signature Neural Network (RS-Net), a deep neural network designed to bridge the gap between the accessibility of RGB sensing and the rich material information provided by spectral data. RS-Net predicts spectral signatures from RGB patches, which we map to terrain labels and friction coefficients. The resulting terrain classifications are integrated into a sampling-based motion planner for a wheeled robot operating in outdoor environments. Likewise, the friction estimates are incorporated into a contact-force-based MPC for a quadruped robot navigating slippery surfaces. Overall, our framework learns the task-relevant physical properties offline during training and thereafter relies solely on RGB sensing at run time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
