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High-fidelity 3D reconstruction for planetary exploration

Alfonso Martínez-Petersen, Levin Gerdes, David Rodríguez-Martínez, C. J. Pérez-del-Pulgar

Abstract

Planetary exploration increasingly relies on autonomous robotic systems capable of perceiving, interpreting, and reconstructing their surroundings in the absence of global positioning or real-time communication with Earth. Rovers operating on planetary surfaces must navigate under sever environmental constraints, limited visual redundancy, and communication delays, making onboard spatial awareness and visual localization key components for mission success. Traditional techniques based on Structure-from-Motion (SfM) and Simultaneous Localization and Mapping (SLAM) provide geometric consistency but struggle to capture radiometric detail or to scale efficiently in unstructured, low-texture terrains typical of extraterrestrial environments. This work explores the integration of radiance field-based methods - specifically Neural Radiance Fields (NeRF) and Gaussian Splatting - into a unified, automated environment reconstruction pipeline for planetary robotics. Our system combines the Nerfstudio and COLMAP frameworks with a ROS2-compatible workflow capable of processing raw rover data directly from rosbag recordings. This approach enables the generation of dense, photorealistic, and metrically consistent 3D representations from minimal visual input, supporting improved perception and planning for autonomous systems operating in planetary-like conditions. The resulting pipeline established a foundation for future research in radiance field-based mapping, bridging the gap between geometric and neural representations in planetary exploration.

High-fidelity 3D reconstruction for planetary exploration

Abstract

Planetary exploration increasingly relies on autonomous robotic systems capable of perceiving, interpreting, and reconstructing their surroundings in the absence of global positioning or real-time communication with Earth. Rovers operating on planetary surfaces must navigate under sever environmental constraints, limited visual redundancy, and communication delays, making onboard spatial awareness and visual localization key components for mission success. Traditional techniques based on Structure-from-Motion (SfM) and Simultaneous Localization and Mapping (SLAM) provide geometric consistency but struggle to capture radiometric detail or to scale efficiently in unstructured, low-texture terrains typical of extraterrestrial environments. This work explores the integration of radiance field-based methods - specifically Neural Radiance Fields (NeRF) and Gaussian Splatting - into a unified, automated environment reconstruction pipeline for planetary robotics. Our system combines the Nerfstudio and COLMAP frameworks with a ROS2-compatible workflow capable of processing raw rover data directly from rosbag recordings. This approach enables the generation of dense, photorealistic, and metrically consistent 3D representations from minimal visual input, supporting improved perception and planning for autonomous systems operating in planetary-like conditions. The resulting pipeline established a foundation for future research in radiance field-based mapping, bridging the gap between geometric and neural representations in planetary exploration.
Paper Structure (14 sections, 4 equations, 3 figures, 3 tables)

This paper contains 14 sections, 4 equations, 3 figures, 3 tables.

Table of Contents

  1. INTRODUCTION
  2. RADIANCE FIELD–BASED METHODS
  3. Neural Radiance Fields (NeRF)
  4. Gaussian Splatting
  5. Comparison of Methods Applied to Space Robotics
  6. METHODOLOGY
  7. Pipeline Description
  8. Experimental Setup
  9. Pipeline Validation
  10. RESULTS
  11. Overview of the Reconstructions
  12. Performance Metrics
  13. Results Evaluation and Limitations
  14. CONCLUSION

Figures (3)

  • Figure 1: Overview of the proposed reconstruction pipeline. Raw rover data (such as RGB images, IMU readings, and GNSS data) are extracted directly from rosbag recordings and preprocessed for geometric reconstruction with COLMAP (SfM). The resulting sparse model and camera poses are then passed to the radiance field stage, where Splatfacto-W performs dense reconstruction through Gaussian Splatting, generating a photorealistic 3D model. Finally, a quality evaluation module computes performance metrics across both geometric and visual dimensions.
  • Figure 2: General aerial views of 3D models reconstructed from rover traverses in BASEPROD. Each model corresponds to a segment of the terrain captured by the MaRTA rover under real outdoor illumination and surface conditions.
  • Figure 3: Comparative examples between real-world views (left) and their corresponding 3D reconstructions (right). The visual alignment demonstrates the high geometric and radiometric consistency achieved by the proposed workflow.