Tianwen-1, China’s first Mars exploration mission, was successfully landed in the southern part of Utopia Planitia on 15 May 2021 (UTC+8). Timely and accurately determining the landing location is critical for the subsequent mission operations. For timely localization, the remote landmarks, selected from the panorama generated by the earliest received Navigation and Terrain Cameras (NaTeCam) images, were matched with the Digital Orthophoto Map (DOM) generated by high resolution imaging camera (HiRIC) images to obtain the initial result based on the triangulation method. Then, the initial localization result was refined by the descent images received later and the NaTeCam DOM. Finally, the lander location was determined to be (25.066°N, 109.925°E). Verified by the new orbital image with the lander and Zhurong rover visible, the localization accuracy was within a pixel of the HiRIC DOM.

Digital elevation models (DEMs) provide fundamental data for scientific and engineering applications in lunar exploration missions. Lunar DEMs have been mainly generated by laser altimetry and stereophotogrammetry. Complementarity to stereo photogrammetry, reflection-based surface reconstruction methods, such as shape from shading (SFS), have been studied and applied in lunar DEM reconstruction from a single image. However, this method often suffers from solution ambiguity and instability. In this paper, we propose a generative adversarial network (GAN)-based method that is able to generate high-resolution pixel-scale DEMs from a single image aided by a low-resolution DEM. We have evaluated the accuracy of the reconstructed high-resolution DEMs from 25 LROC NAC images of four regions using LROC NAC DEMs (2 m/pixel) as ground truth. The experimental results demonstrate good accuracy and adaptability to changes in illumination conditions. The root mean square error (RMSE) can reach about 2 m in areas where the degree of elevation variation is less than 100 m, and the RMSE value ranges from around 3 m to 10 m without considering the degree of the elevation variation in large-area reconstruction. As high-resolution monocular images and low-resolution DEMs are available for the entire lunar surface, the proposed GAN-based method has great potential in high-resolution lunar DEM reconstruction for lunar mapping applications.

This paper presents the methods, results and applications of visual localization and topographic mapping for Zhurong rover in China's Tianwen-1 mars mission. New techniques, namely vignette correction and online camera mast calibration, were developed for the images captured by the rover's Navigation and Terrain Cameras (NaTeCam) to enhance the mapping qualities. During surface operations, photogrammetric techniques were routinely applied at the travelling waypoints. The NaTeCam images were obtained to generate 3D topographic mapping products with centimeter level to support obstacle analysis and rover path planning. Cross-site visual localization at adjacent waypoints was conducted to reduce the accumulated position errors in the onboard dead-reckoning result. Visual localization refinement was also employed by matching of the digital orthophoto map (DOM) from the rover images and the orbital basemap at waypoints where distinct terrain features were available to further eliminate the localization errors. Comparing with the reference rover traverse extracted from a HiRISE image in which the actual rover track is visible, the accuracies of the rover traverses generated from cross-site visual localization and DOM matching refinement are estimated to be 1.75% and 0.50% of the traverse length. The actual total traverse length is 2009 m, which is 88 m longer than 1921 m from odometer reading, revealing a forward wheel slipping caused by the downslope trend of the southward traverse. The visual localization and topographic mapping results of Zhurong rover have greatly supported the surface exploration planning, object approaching and scientific investigations in the mission.

High-precision localization of the Mars rovers is fundamental for path planning and safe navigation towards exploration targets during Mars missions. In cross-site visual localization, image matching is the key step to obtain corresponding points connecting images from different sites. The cross-site visual localization method based on Affine SIFT used in Tianwen-1 mission but is constrained in regions of Mars with poor texture and large viewpoint invariance. In this paper, we propose a cross-site visual localization methodology of Mars rover based on self-supervised keypoint extraction and robust matching. The self-supervised keypoint extraction network, which is called MRSS-Net, employs multi-scale deformable structures (MSDS) during the feature encoding stage to enhance the network’s ability of extracting invariant features in regions with large viewpoint variations and improve the rate of identical points for cross-site images with poor texture. In addition, we develop self-attention descriptor enhancement mechanism (SADEM) to distinguish local features in repetitive patterns. The robust matching, which is called adaptive 2D-3D matching, employs GNC dead reckoning (3D priori information) to construct the initial coarse matching domain and homography matrix (2D information) to construct a progressively shrinking refined matching domain. We compared our method against Affine SIFT based cross-site visual localization model, as well as advanced deep learning algorithms and evaluate the performance using NaTeCam images collected during the traversal of four long-distance traversals (a total of 44 Martian sol sites) by Zhurong rover. The experimental results show that our framework reduces the localization error by 12.5% and improves localization robustness by 50.8%, compared to Affine SIFT based cross-site visual localizaztion method used in Zhurong rover. Additionally, our method outperforms state-of-the-art deep-learning techniques and ensures the current accuracy of cross-site visual localization for Mars rover, while significantly increasing the level of automation.

Lunar global mapping products provide a solid data foundation for lunar scientific research and exploration. As the widespread geometric inconsistencies among multi-source mapping products seriously affect the synergistic use of the products, high-precision registration of multiple lunar global products is critical, and it is highly challenging due to large coverage and complex local geometric inconsistencies. In this research, we propose a spherical triangular-mesh-based method for high-precision registration of lunar global mapping products, which involves four steps: data preprocessing, feature point extraction and matching, spherical Delaunay triangulation, and geometric correction with spherical barycentric coordinates. This global registration method avoids map projection distortions by using spherical coordinates directly, and achieves high precision by confining the geometric models to spherical triangular facets. Experiments are conducted using two groups of different types of mapping products to verify the proposed method quantitatively and qualitatively. The results show that the geometric inconsistencies are reduced from hundreds of pixels to sub-pixel level globally after the registration, demonstrating the effectiveness of the proposed method.

High-precision localization of Mars rovers is essential for safe path planning and efficient navigation toward scientific targets. As planetary rovers traverse the surface, their positional uncertainty accumulates, which can be corrected through global localization by registering rover images to orbital maps. To date, image-based solutions are widely adopted; however, substantial manual intervention is often required, which is time-consuming and limits the range of autonomous navigation. To address these challenges, we propose a two-stage localization framework, comprising the Mars cross-view few-shot training paradigm (MarsCVFP), Mars cross-view feature extraction network (MCVN) trained under MarsCVFP, and a robust template matching algorithm. Specifically, the MarsCVFP model can leverage implicit cross-view feature as guidance without relying on a large amount of high-precision location-level supervision and explicitly annotated, specific learning targets in the scene. MCVN can capture discriminative fine-grained features on the weakly textured and unstructured surface of Mars by constructing the multi-scale feature pyramid structure (MSFPS) and the feature interaction module (FIM). We validate our framework on 85 unit-planned sites and 20 panoramic sites, respectively, as traversed by the Zhurong rover. The experimental results demonstrate that our framework consistently outperforms both the traditional approaches and the representative learning-based methods across diverse terrains, including dunes, bedrock, craters, and flat plains, achieving a localization success rate above 82% while maintaining a localization accuracy of better than 4 pixels, even under coarse prior positions uncertainties spanning 40 m × 40 m.