Conference Agenda

Asteroid exploration is crucial for understanding the solar system’s origin, but establishing a precise body-fixed coordinate system—relying on accurate rotation parameters—remains challenging. Conventional methods like ground-based light curve inversion often lack precision: for example, it yielded ±2° errors for Ceres’ pole and ±10° for Vesta’s, failing to meet demands for topographic mapping and navigation. This study proposes a refinement method combining stereo intersection angle optimization and grayscale threshold masking. First, using the camera’s interior orientation parameters and tie point coordinates, relative orientation of stereo image pairs is conducted to build a stereo model, followed by forward intersection to calculate intersection angles. Only pairs with favorable geometry (intersection angle >5°) are retained to avoid large position errors from nearly parallel sightlines. Second, a grayscale-based binary mask is created to separate the asteroid from the deep-space background, eliminating spurious edge features that cause mismatches; the SIFT algorithm then extracts and matches features exclusively within the masked region. Finally, an “exhaustive search” iteratively refines rotation parameters using optimized matched points. Validated on 127 Hayabusa2 ONC-T images of asteroid Ryugu (captured July 10, 2018, 2.11m/pixel), the method reduced 5,174 initial candidate pairs to 1,454 valid ones (137,191 matched points). After 4 iterations, refined parameters were RA=96.5° and Dec=-66.4°, with minimal errors (δRA=0.069°, δDec=0.0126°) against reference values (RA=96.431°, Dec=-66.387°). Compared to methods without the two strategies, mismatches dropped from 14,949 to 7,369, and forward intersection residuals decreased. Future work will integrate initial parameters into a bundle adjustment model for further refinement.

The lunar polar regions have emerged as core targets in lunar exploration, primarily due to the potential water ice resources stored within their permanently shadowed areas. However, the complex terrain and extreme illumination conditions in these polar regions present significant challenges to the navigation of lunar rovers—systems that previously relied on dead reckoning and visual matching techniques. To address this, active 3D sensors such as LiDAR will be integrated into future exploration missions.Simultaneous Localization and Mapping (SLAM) based on multi-sensor fusion via factor graphs can significantly enhance the localization robustness of rovers on the lunar surface. In this context, we propose the Lunar Scene Recognition Adaptive SLAM (LSRA-SLAM) method: a framework that leverages environment-aware pre-training to dynamically adjust factor-graph weights, thereby achieving more consistent fusion of stereo camera, LiDAR, and IMU measurements across diverse lunar scenarios. We also introduce a reinforcement learning-based online training strategy, which enables the network to robustly learn from the system's dynamic behaviors. Simulated experiments validate the effectiveness of the proposed LSRA-SLAM method.

Accurate crater mapping in lunar permanently shadowed regions (PSRs) is hindered by extreme low-light and low-resolution imagery. We present YOLOLens2.0, a unified, end-to-end deep learning framework designed for high-fidelity crater detection and terrain reconstruction in these challenging environments. The architecture integrates a Dense-Residual-Connected Transformer

(DRCT) for multimodal super-resolution (SR) with a YOLO-derived detection module and an affine calibrator to ensure geometric consistency at meter scale. Our framework exploits a bidirectional synergy where SR enhances feature discriminability for detection, while detection-driven supervision refines structural reconstruction. Validation on Kaguya data demonstrates a significant performance leap, achieving a Recall of 89.20% and an mAP@50 of 0.844 an improvement of over 33 percentage points in recall compared to the original YOLOLens. Out-of-distribution validation on ShadowCam imagery, performed without fine-tuning, confirms the model’s robustness and scalability. The framework successfully preserves quantitative elevation fidelity and supports detailed morphometric analyses, including the extraction of the crater size-frequency distributions (SFDs) that align with theoretical lunar production functions. YOLOLens2.0 provides a scalable, high-precision methodology for planetary mapping, offering critical insights for lunar surface evolution studies and future exploration missions.

Reliable localization across the full orbit-descent-ground chain in planetary exploration remains difficult because extreme differences in altitude, viewing geometry, resolution, and illumination cause cross-view image matching to fail. Traditional keypoint pipelines and unified Structure-from-Motion (SfM) struggle to establish robust correspondences across these heterogeneous Satellite-Descent-Ground datasets due to severe domain gaps.

To overcome these limitations, we propose a novel framework based on a joint semantic-geometric optimization paradigm. Rather than forcing a unified SfM pipeline across drastically different viewpoints, our method leverages independent intra-domain SfM outputs and telemetry data as structural priors. We introduce a differentiable rendering approach that tightly couples the optimization of 3D Gaussian Splatting (3DGS) scene parameters with learnable camera extrinsics. Furthermore, by integrating high-level semantic epipolar constraints derived from foundation models, our method dynamically refines initial cross-domain pose estimates during the rasterization loop. This joint formulation effectively bypasses the fragility of low-level pixel matching, enabling accurate and robust alignment across the vast baselines inherent to multi-stage planetary exploration image sequences.

Mars topographic data are crucial for quantitatively characterizing the Martian surface, supporting exploration missions, and enabling scientific study of surface processes. Photogrammetric processing of Mars orbital imagery is the most representative method for generating 3D terrain models, with bundle adjustment (BA) serving as the key step for mitigating inconsistencies in overlapping regions of different orbital images and further improving the spatial accuracy of the resulting DTMs. However, due to the texture-less surface of Mars and the absence of ground control points, the stability of BA is often compromised. Impact craters, which are prevalent on the Marian surface, have been utilized as an important semantic prior in various image analysis applications. They can also be used to assist the BA process for precision topographic mapping of the Martian surface.

This study introduces a novel BA method assisted by robust crater graph features to address this. The approach involves: (1) extracting craters using a deep learning model (YOLOv5) and constructing a stable graph structure via a minimum spanning tree; (2) establishing crater correspondences across different images based on graph features to generate robust tie points; and (3) formulating a strengthened BA equation with constraints from the graph's angular and edge relationships to mitigate geometric inconsistencies.

Experimental results indicate that the proposed method provides an effective solution for high-precision 3D mapping from Martian surface imagery with limited textures and significant illumination variation. By incorporating crater graph features, it enhances the precision and stability of BA, yielding high-precision topographic mapping results for various applications.

Surface roughness influences image contrast by altering illumination, which depends on the surface slope. We conducted Monte Carlo simulations of rough surfaces under both directly illuminated and secondary-illuminated lunar conditions. Our results indicate that PSR secondary illumination yields significantly lower contrast, characterized by soft, diffuse shading and negligible shadow fraction.