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This paper introduces a real-time lunar surface mapping framework using 3D Gaussian Splatting (3DGS) integrated with dense perception models. They benchmarked stereo depth estimation (GRU-based) and semantic segmentation (CNN-based) models on synthetic lunar datasets, finding a combination that balances speed and accuracy. The resulting pipeline achieved 3cm height accuracy over a 120-meter traverse, outperforming point cloud baselines and demonstrating the efficacy of learned map representations for lunar exploration.
Ditch LiDAR: 3D Gaussian Splatting, combined with semantic segmentation and stereo depth, enables real-time lunar mapping with centimeter-level accuracy.
Navigation and mapping on the lunar surface require robust perception under challenging conditions, including poorly textured environments, high-contrast lighting, and limited computational resources. This paper presents a real-time mapping framework that integrates dense perception models with a 3D Gaussian Splatting (3DGS) representation. We first benchmark several models on synthetic datasets generated with the LuPNT simulator, selecting a stereo dense depth estimation model based on Gated Recurrent Units for its balance of speed and accuracy in depth estimation, and a convolutional neural network for its superior performance in detecting semantic segments. Using ground truth poses to decouple the local scene understanding from the global state estimation, our pipeline reconstructs a 120-meter traverse with a geometric height accuracy of approximately 3 cm, outperforming a traditional point cloud baseline without LiDAR. The resulting 3DGS map enables novel view synthesis and serves as a foundation for a full SLAM system, where its capacity for joint map and pose optimization would offer significant advantages. Our results demonstrate that combining semantic segmentation and dense depth estimation with learned map representations is an effective approach for creating detailed, large-scale maps to support future lunar surface missions.
