High-precision localization of the Mars rovers is fundamental for path planning and safe navigation toward 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 (ASIFT) is used in Tianwen-1 mission but is constrained in regions of Mars with poor texture and large viewpoint invariance. In this article, 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, uses multiscale deformable structures (MSDSs) 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 2-D–3-D matching, uses GNC dead-reckoning (3-D priori information) to construct the initial coarse matching domain and homography matrix (2-D information) to construct a progressively shrinking refined matching domain. We compared our method against ASIFT based cross-site visual localization model and 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 with ASIFT-based cross-site visual localization method used in Zhurong rover. In addition, 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.

Morphology of small fresh craters on the Moon may reflect target properties, and the related studies are significant for selection of the landing sites in future missions. Among the morphological characteristics of small fresh craters, the depth-to-diameter is the most important and has been widely studied in previous research. In this research, with the recently acquired high resolution images by LRO mission, we expand the study into the small fresh crater with their diameter in sub-kilometer or even tens of meters. The results show that, relative to the larger craters with their diameter larger than 1 km, the ratio of the small craters decrees from ~0.2 to ~0.1, whether they are in lunar mares or in highlands. The lower ratio is possibly resulted by the low strength of the target for the small craters, and the detailed mechanism should be further studied in future.

Chang$^{\prime}$E-2 (CE-2) lunar orbiter is the second robotic orbiter in the Chinese Lunar Exploration Program. The charge-coupled-device (CCD) camera equipped on the CE-2 orbiter acquired stereo images with a resolution of less than 10 m and global coverage. High-precision topographic mapping with CE-2 CCD stereo imagery is of great importance for scientific research, as well as for the landing preparation and surface operation of the incoming Chang$^{\prime}$E-3 lunar rover. Uncertainties in both the interior orientation (IO) model and exterior orientation (EO) parameters of the CE-2 CCD camera can affect mapping accuracy. In this paper, a self-calibration bundle adjustment method is proposed to eliminate these effects by adding several parameters into the IO model and fitting EO parameters using a third-order polynomial. The additional IO parameters and the EO polynomial coefficients are solved as unknowns along with ground points in the adjustment process. A series of strategies is adopted to ensure the robustness and reliability of the solution. Experimental results using images from two adjacent tracks indicated that this method effectively reduced the inconsistencies in the image space from approximately 20 pixels to subpixel. Topographic profiles generated using unadjusted and adjusted CE-2 data were compared with Lunar Orbiter Laser Altimeter data. These comparisons indicated that the local topographies generated after bundle adjustments, which reduced elevation differences by 9–10 m, were more consistent with LOLA data.

High-resolution (submeter) orbital imagers have opened up new possibilities for Mars topographic mapping with unprecedented precision. While the typical sensor model for Martian orbiters has been the linear-array charge-coupled device (CCD), the High-Resolution Imaging Science Experiment (HiRISE) instrument is based on a more complicated structure involving a combination of 14 separate linear-array CCDs. To take full advantage of this high-resolution capability without compromising imaging geometry, we have developed a rigorous photogrammetric model for HiRISE stereo image processing in which third-order polynomials are used to model the change in exterior-orientation parameters over time. A coarse-to-fine hierarchical matching approach was developed, and its performance was evaluated based on manually matched image points and manually measured features for a test area at the Mars Exploration Rover Spirit landing site. Using automatically selected tie points, we performed bundle adjustment (BA) to improve the accuracy of image pointing data and remove or reduce inconsistencies between the stereo pair and inconsistencies between overlapping CCDs in the same image mosaic. A method for the incorporation of jitter terms into the BA was developed and proved to be effective. We created a 1-m-resolution digital elevation model and an orthophoto using this methodology and compared them with topographic products from the U.S. Geological Survey.