Deep space investigation has gradually developed from remote sensing to planet surface exploration, and rover survey is the main way for planet surface exploration. Precise localization is essential to ensure the safety of the rover. In the past decades, manual-assisted station visual positioning has been extensively employed, while recent studies on track reckoning and visual odometry have provided a foundation for the advancement of automated localization of rovers. However, the cumulative error generated by long-distance exploration is difficult to avoid without global constraints. In this work, we propose a coarse-to-fine localization method wherein craters are employed as landmarks. The orientation of craters captured by the camera serves as positional information to guide a grid search and matching process within the map, achieving initial coarse localization. The precise edges of the craters are extracted from the Panoramic Camera (PCAM) images and aligned with the crater rim in the map to refine the localization result, thereby enabling fine localization of the rover. To validate the proposed method, we conducted experiments on datasets from the Chang'e-3 and Chang'e-4 lunar missions, as well as a simulated dataset containing fifty image pairs constructed from a local digital elevation model (DEM) of the lunar surface. The results demonstrate that, starting from an initial estimate with an error margin of less than 50 meters, our method significantly enhances localization accuracy, achieving meter-level precision, with most results concentrated within approximately 2 meters. Furthermore, the algorithm operates at a sub-second processing speed, which ensures its execution during the rover's panoramic imaging phase between motion and stop, thereby satisfying the real-time localization requirements of actual mission scenarios.

The structural stability of lava tubes is a critical factor for their potential use in lunar base construction. Previous studies could not reflect the details of lava tube boundaries and perform accurate mechanical analysis. To this end, this study proposes a robust method to construct a high-precision, real-scene 3D model based on ground lava tube point cloud data. By employing finite element analysis, this study investigated the impact of real-world cross-sectional geometry, particularly the aspect ratio, on structural stability under surface pressure simulating meteorite impacts. A high-precision 3D reconstruction was achieved using UAV-mounted LiDAR and SLAM-based positioning systems, enabling accurate geometric capture of lava tube profiles. The original point cloud data were processed to extract cross-sections, which were then classified by their aspect ratios for analysis. Experimental results confirmed that the aspect ratio is a significant factor in determining stability. Crucially, unlike the monotonic trends often suggested by idealized models, analysis of real-world geometries revealed that the greatest deformation and structural vulnerability occur in sections with an aspect ratio between 0.5 and 0.6. For small lava tubes buried 3 m deep, the ground pressure they can withstand does not exceed 6 GPa. This process helps identify areas with weaker load-bearing capacity. The analysis demonstrated that a realistic 3D modeling approach provides a more accurate and reliable assessment of lava tube stability. This framework is vital for future evaluations of lunar lava tubes as safe habitats and highlights that complex, real-world geometry can lead to non-intuitive structural weaknesses not predicted by simplified models.

The lack of high-precision imaging data for lunar volcanic regions currently hinders the detailed characterization of lava tube systems and their associated fine-scale geomorphology. To address this information deficit, this study establishes the Jingpo Lake Volcanic Field (JLVF) in Northeast China as a primary terrestrial analog for the lunar Marius Hills complex. We systematically characterize the basaltic morphometric continuum, tracing the geological evolution from proximal scoria cones through medial lava tube skylights to distal lava plateaus. Focusing on the subsurface transport system, we identify a linear chain of discontinuous skylights that structurally mirrors the “proto-rille” stage of lunar sinuous rilles. Quantitative morphometry reveals that these terrestrial vents reproduce the geometric duality of lunar pits, ranging from stable “deep shafts” to degraded “funnel pits,” effectively validating the mechanical diversity of the lunar inventory. Critically, the “U-to-V” cross-sectional transition observed in JLVF collapse trenches serves as diagnostic ground-truth evidence, confirming that lunar rilles originate from the catastrophic roof failure of subsurface tubes rather than purely thermal erosion. Regarding the lava plateau, our field investigation resolves sub-meter micro-textures—including laminar pahoehoe ropes and inflation fissures—that are typically obscured by the resolution limits of current lunar orbiters. These findings suggest that the seemingly “smooth” lunar maria likely host complex, rugged micro-terrains. Therefore, comparing lunar volcanic regions with simulated volcanic fields from Earth is crucial. Analyzing potential volcanic products from angles undetectable by some lunar satellites can offer vital insights for future lunar exploration.

The calibration of the camera and LiDAR is one of the basis for the construction of the multi-sensor fusion mapping system. Planar features of walls and grounds in indoor environments provides effective constraints for multi-sensor calibration. In this paper, we proposed a new camera-LiDAR calibration method with the constraint of indoor spatial structure. Using the image and point cloud data collected by sensors, visual odometry and LiDAR odometry can be constructed to calculate the transformation between sensors. Based on visual odometry and LiDAR odometry, structural parameters in indoor environment are extracted from images and point cloud to constrain rotation estimation between sensors. In the method proposed in this paper, lines are extracted from the images acquired by the camera and used to estimate and track vanishing points. The direction estimated with vanishing points is used as a global constraint to optimize the rotation parameter estimation of the camera. The fitted planes from the point cloud acquired by the LiDAR are used to compute a set of orthogonal normal vectors corresponding to the ground and wall surfaces, which are used as global constraints to optimize the rotation parameter estimation of the LiDAR. The calibration method proposed in this paper is targetless and only constrained by the indoor spatial structure. The result shows that the proposed indoor spatial structure constraint calibration method can calibrate LiDAR and camera without generating cumulative errors during the rotation estimation process.

The Low-Altitude Economy (LAE), driven by the widespread deployment of UAVs and eVTOL aircraft, demands a high-fidelity Digital Twin that extends far beyond static geographic representation. This study presents a critical review of 39 peer-reviewed papers to propose a three-layer mapping framework — Geospatial Infrastructure Layer, Environmental Sensing Layer, and Interaction Layer — and evaluates the Technology Readiness Level (TRL) of each sub-domain. The Geospatial Infrastructure Layer encompasses terrain models, ground facilities, airspace structures, and semantic navigation landmarks. The Environmental Sensing Layer covers electromagnetic modeling, target sensing and countermeasure, and micro-meteorological mapping. The Interaction Layer addresses network trust, data security, swarm coordination, and platform reliability. Our TRL assessment reveals that Environmental Sensing is the most mature layer (mean TRL 4.4, 8 field-validated papers), while cross-layer integration remains the weakest link (mean TRL 3.5, zero field-validated demonstrations). We identify standardization of low-altitude spatial data products, AI-enabled predictive mapping, crowdsourced Digital Twin updating, and closed-loop cross-layer integration as the four priority research directions.