Monocular Visual SLAM With Adjusting Neural Radiance Fields for 3-D Reconstruction in Planetary Environments

In planetary environments, conducting autonomous exploration tasks requires rovers to autonomously navigate the scene and achieve a detailed understanding of the terrain. Vision-based simultaneous localization and mapping (SLAM), which utilizes compact and low-power visual sensors for autonomous exploration, offers significant advantages in hardware deployment. While several methods have been proposed to apply visual navigation in planetary scenarios, they often rely on aerial imagery from orbiters and high-resolution digital elevation models (DEMs) for assistance. In addition, accurate camera poses are typically required for dense matching during scene reconstruction, and the inability to perform loop closure significantly limits the performance of visual SLAM. Here, we propose a monocular visual SLAM approach combined with an adjusted neural radiance field (NeRF) for autonomous navigation and 3-D reconstruction in planetary environments. Our approach solely relies on visual images as input and leverages the powerful learning capabilities of NeRFs to adapt to unseen scenes while simultaneously regressing both camera poses and scene representations. The estimated depth maps and poses can be further used for 3-D reconstruction, assisting planetary exploration missions. The proposed method was tested on the Devon and MADMAX datasets that simulate planetary environments and achieved remarkable results. Even under the fixed rover navigation perspective, our pose estimation accuracy outperforms classical visual SLAM and other deep learning-based SLAM methods. In addition, our novel view synthesis results exhibit quality comparable to those in terrestrial scenes. Comparisons with multiview stereo (MVS) techniques in terms of 3-D reconstruction demonstrate that our approach recovers finer surface details. We also applied our method to the Perseverance rover dataset and achieved satisfactory positioning and reconstruction results in a real Martian environment, proving the practical feasibility of our method.