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MARE: An Open-Source Lunar Rover Simulator with Shadow Ground Truth for Perception and Autonomy

Raúl Castilla-Arquillo, Miguel A. Olivares-Méndez · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28465 · cited by 0 works (OpenAlex) · open-access PDF

German

Lunar Solar Stereo Surface Exploration stützt sich zunehmend auf autonome Rover, die unstrukturiertes Gelände unter extremen Beleuchtungsbedingungen wahrnehmen und durchqueren müssen. In der Nähe der Mondpolen erzeugen niedrige Sonnenoberflächen lange, hochkontrastreiche Schatten, die Stereo-Vision, Terrain-Segmentierung und Lokalisierung herausfordern. Die Entwicklung von Wahrnehmungs- und Navigationsalgorithmen für diese Bedingungen erfordert große Mengen von Sensordaten mit präziser Bodenwahrheit, die Feldkampagnen und analoge Einrichtungen nur zu hohen Kosten und mit begrenzter Vielfalt liefern können. Dieses Papier präsentiert MARE (Modular Autonomy and Rendering Environment), eine Open-Source-Simulationssuite für Mondrover-

Machine translation (Meta M2M100); the English original follows.

Abstract

Lunar surface exploration increasingly relies on autonomous rovers that must perceive and traverse unstructured terrain under extreme illumination conditions. Near the lunar poles, low sun elevations produce long, high-contrast shadows that challenge stereo vision, terrain segmentation, and localization. Developing perception and navigation algorithms for these conditions requires large amounts of sensor data with accurate ground truth, which field campaigns and analog facilities can only provide at high cost and with limited variety. This paper presents MARE (Modular Autonomy and Rendering Environment), an open-source simulation suite for lunar rover perception and autonomy built on Unreal Engine 5 and ROS 2. MARE consists of three integrated components. Terrain Studio generates cratered and rocky lunar Digital Elevation Maps (DEMs) to build simulation scenes. A dedicated Unreal Engine project hosts two physics-based rovers with plugins for stereo, lidar, event, and Single-Photon Avalanche Diode (SPAD) cameras, streaming time-stamped sensor data and pixel-accurate ground truth. Lastly, a Guidance, Navigation, and Control (GNC) stack provides web-based mission control. Validation shows a mean Intersection over Union (IoU) of 0.79 between predicted and rendered shadows at low sun elevations, and stereo depth analysis verifies decreased pixel coverage in shadowed regions relative to sunlit terrain.

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Topics: Terrain, Rendering (computer graphics), Computer science, Traverse, Ground truth, Remote sensing

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