PRISM: 不整地環境におけるローバー航法のためのマルチモーダル地形マッピング
PRISM: Multimodal Terrain Mapping for Rover Navigation in Unstructured Environments
RGB・深度・熱画像を統合し、視覚変換器ベースのネットワークで地形をセグメンテーションして走行可能性マップを生成する、ローバー航法用のマルチモーダル認識システムを提案した。
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著者: Raul Castilla-Arquillo, Carlos Perez-del-Pulgar, Levin Gerdes, Alfonso Garcia-Cerezo, Miguel A. Olivares-Mendez
分類: cs.RO, cs.AI
原文アブストラクト
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.
