Visual SLAM with DEM Anchoring for Lunar Surface Navigation

Abstract

Future lunar missions will require autonomous rovers capable of traversing tens of kilometers across challenging terrain while maintaining accurate localization and producing globally consistent maps. However, the absence of global positioning systems, extreme illumination, and low-texture regolith make long-range navigation on the Moon particularly difficult, as visual-inertial odometry pipelines accumulate drift over extended traverses. To address this challenge, we present a stereo visual simultaneous localization and mapping (SLAM) system that integrates learned feature detection and matching with global constraints from digital elevation models (DEMs). Our front-end employs learning-based feature extraction and matching to achieve robustness to illumination extremes and repetitive terrain, while the back-end incorporates DEM-derived height and surface-normal factors into a pose graph, providing absolute surface constraints that mitigate long-term drift. We validate our approach using both simulated lunar traverse data generated in Unreal Engine and real Moon/Mars analog data collected from Mt. Etna. Results demonstrate that DEM anchoring consistently reduces absolute trajectory error compared to baseline SLAM methods, lowering drift in long-range navigation even in repetitive or visually aliased terrain.

Figures (6)

• Figure 1: LightGlue feature matching between left and right stereo images from our Unreal Engine lunar simulation. Features are detected and matched reliably despite challenging illumination conditions. A maximum of 200 matches are plotted to reduce clutter.
• Figure 2: Example data from each dataset. Top: sample rover imagery. Bottom: corresponding digital elevation models (DEMs). From left to right: LuSNAR, S3LI, Unreal Engine simulation.
• Figure 3: Opportunity rover model opportunity_rover_asset used in our custom Unreal Engine simulation.
• Figure 4: Trajectories from LuSNAR scene 9. Ground-truth trajectory is shown in green, estimated VO trajectory in red, and DEM-anchored SLAM in blue. DEM anchoring successfully constrains the SLAM trajectory to the surface and mitigates vertical drift.
• Figure 5: S3LI crater loop with loop closure. Black: ground truth. Blue: before loop closure. Red: after loop closure. The loop closure connection (from start to end) is indicated with a straight red line. Left: loop closure without DEM anchoring leads to significant distortion. Right: DEM anchoring regularizes loop closure, mitigating drift and preventing catastrophic deformation. Residual deviation remains due to limited loop-closure constraints and the fact that DEM height and normal cues alone do not fully constrain tangential motion along visually aliased terrain.