PRISM: Multimodal Terrain Mapping for Rover Navigation in Unstructured Environments
PRISM is a multimodal perception system that integrates thermal, optical, and depth sensors with a novel vision transformer network (OmniUnet) for terrain segmentation and traversability mapping. Validated on two new datasets and deployed on an embedded computer, it enables autonomous rover navigation in challenging terrain.
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[Submitted on 17 Jul 2026]
Title:PRISM: Multimodal Terrain Mapping for Rover Navigation in Unstructured Environments
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Abstract: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.
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.16366 [cs.RO]
(or arXiv:2607.16366v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.16366
arXiv-issued DOI via DataCite
Submission history
From: Raúl Castilla-Arquillo [view email] [v1] Fri, 17 Jul 2026 13:49:59 UTC (31,692 KB)
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