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.

The centers of matching craters can be beneficial additions to the control point database. Crater matching on heterogeneous surfaces is helpful in testing its applicability to the entire moon. Therefore, we propose a coarse-to-fine crater matching method for heterogenous surfaces on images acquired from the narrow angle camera (NAC) of the lunar reconnaissance orbiter camera (LROC) and the Chang’e-2 digital orthophoto map (DOM). First, we perform coarse matching based on the Hausdorff distance using the area and coordinates of the crater. Then, the mismatched craters are removed by using the affine transform model fitted by corresponding points of mutual information matching. Finally, we use the retained matched crater centers to fit the affine transformation model between the images, predict the corresponding position, and obtain the corresponding crater around it to achieve fine matching. The results show that the proposed method obtains numerous crater matches on images covering different terrains and solar altitude angles compared to the Hausdorff distance-based crater matching method. For the five experimental scenes registered using matched craters, the mean values of the checkpoints are approximately 2 and 3 pixels for scenes with small and large differences from Chang’e-2 solar altitude angles, respectively, and the standard deviations (STDs) for both are approximately 1 pixel. In addition, the highlands have lower accuracy than the maria, with a variance of less than 1 pixel. Furthermore, the registration accuracy is related to the diameter and number of craters.

Fine-scale 3-D morphological features of rocks on the Martian surface provide important clues to Mars exploration missions and scientific discoveries. To obtain such information, an automatic approach for high-precision measurement of 3-D morphological features of Martian rocks is proposed in this letter. The approach directly detects 3-D rocks from dense point cloud that is generated by interpolating triangulated irregular network (TIN) model, through a coarse-to-fine process combining cloth simulation filtering (CSF) and connected-component labeling algorithms. Multiple 3-D morphological features are then precisely extracted by modeling rock points and fitting local ground plane. Experimental results demonstrate that the proposed approach provides an effective way for the extraction of complete 3-D rocks and comprehensive morphological features with over 90% accuracy.

In massive communication networks, bursty traffic may cause an unexpected network overload. Traditionally, the access class barring (ACB) based random access (RA) schemes perform well in controlling network overload. However, as the number of contention devices increases, the performance of the ACB-based access control mechanism degrades severely, especially in terms of access success probability and average access delay. In recognition of the fact that existing ACB-based RA scheme does not take the influence of the physical random access channel (PRACH) state on RA performance into consideration, in this paper, we propose a joint RA control scheme involving two access check mechanisms, i.e., ACB check and PRACH channel quality check. As a result, the proposed joint scheme can not only reduce the number of contention de-vices in each RA slot through ACB check but also guarantee the success of preamble (PA) detection by means of PRACH quality check. Simulation results show that the proposed scheme outperforms the other ACB-based RA scheme in terms of access success probability, network load capacity, and the average number of PA transmission at the expense of increased total service time. As a result, compared with other access control mechanisms, the proposed scheme is more suitable for delay-tolerate services.

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.

This letter presents a new technique for clustering hyperspectral images that exploits neighborhood-constrained spatial information. The main feature of the proposed method is the introduction of a neighborhood homogeneity index (NHI) and the use of this index to measure the spatial homogeneity in a local area. A new similarity measurement integrates NHI and spectral information using an adaptive distance norm for clustering. The performance of the proposed neighborhood-constrained-clustering algorithm was assessed through a synthetic image and a real hyperspectral image and compared with those obtained by advanced spectral-spatial clustering algorithms. Experimental results show that the proposed scheme gives better performances.

An adaptive Markov random field (MRF) approach is proposed for classification of hyperspectral imagery in this letter. The main feature of the proposed method is the introduction of a relative homogeneity index for each pixel and the use of this index to determine an appropriate weighting coefficient for the spatial contribution in the MRF classification. In this way, overcorrection of spatially high variation areas can be avoided. Support vector machines are implemented for improved class modeling and better estimate of spectral contribution to this approach. Experimental results of a synthetic hyperspectral data set and a real hyperspectral image demonstrate that the proposed method works better on both homogeneous regions and class boundaries with improved classification accuracy.