发表机构
SnT, University of Luxembourg; Department of Automation and Systems Engineering, Universidad de Málaga, Andalucía Tech（卢森堡大学安全、可靠性和信任跨学科中心; 马拉加大学自动化与系统工程系，安达卢西亚技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对非结构化环境中漫游车导航问题，提出PRISM多模态感知系统，利用定制传感器套件和OmniUnet网络，经新数据集验证及现场实验，可在资源受限设备上高效生成可通行性地图，实现漫游车自主导航。
AI 中文摘要
非结构化环境中的机器人导航需要强大的态势感知能力，以安全穿越陡坡和岩石地形等危险。为应对这一挑战，感知系统越来越依赖多模态传感器融合。本文提出PRISM，一种用于非结构化环境地形映射的多模态感知系统。它利用定制传感器套件捕获对齐的RGB、深度和热（RGB-D-T）图像，核心是OmniUnet。通过两个新注释数据集验证，并经物理现场实验证明其在现实世界中的适用性，能在资源受限嵌入式计算机上高效生成可通行性地图，助力漫游车自主导航。
英文摘要
Robotic navigation in unstructured environments requires robust situational awareness to safely traverse hazards such as steep slopes and rocky terrain. To address this challenge, perception systems increasingly rely on multimodal sensor fusion. Specifically, integrating thermal imagery with standard optical and depth sensors enhances terrain differentiation, directly improving the reliability of mapping algorithms. This paper presents PRISM, a multimodal perception system for terrain mapping in unstructured settings. PRISM leverages a custom sensor suite to capture aligned RGB, depth, and thermal (RGB-D-T) imagery. At its core is OmniUnet, a novel vision transformer-based network specifically designed for multimodal semantic terrain segmentation. We validated the proposed system using two newly annotated datasets (BASEPROD and LAENTIEC) and demonstrate its real-world applicability through physical field experiments. Deployed on a resource-constrained embedded computer, PRISM efficiently generates traversability maps that directly enable autonomous navigation via a rover's Guidance, Navigation, and Control (GNC) subsystem.
