Chang’e-5, China’s first unmanned lunar sample-return mission, was successfully landed in Northern Oceanus Procellarum on 1 December 2020. Determining the lander location precisely and timely is critical for both engineering operations and subsequent scientific research. Localization of the lander was performed using radio-tracking and image-based methods. The lander location was determined to be (51.92°W, 43.06°N) by both methods. Other localization results were compared for cross-validation. The localization results greatly contributed to the planning of the ascender lifting off from the lander and subsequent maneuvers, and they will contribute to scientific analysis of the returned samples and in situ acquired data.

Chang’e-6 (CE-6) is the first sample-return mission from the lunar farside and will be launched in May of 2024. The landing area is in the south of the Apollo basin inside the South Pole Aitken basin. Statistics and analyses of impact craters in the landing area are essential to support safe landing and geologic studies. In particular, the crater size–frequency distribution information of the landing area is critical to understanding the provenance of the CE-6 lunar samples to be returned and can be used to verify and refine the lunar chronology model by combining with the radioisotope ages of the relevant samples. In this research, a digital orthophoto map (DOM) mosaic with resolution of 3 m/pixel of the CE-6 landing area was generated from the 743 Narrow Angle Camera of the Lunar Reconnaissance Orbiter Camera. Based on the DOM, craters were extracted by an automated method and checked manually. A total of 770,731 craters were extracted in the whole area of 246 km × 135 km, 511,484 craters of which were within the mare area. Systematic analyses of the crater distribution, completeness, spatial density, and depth-to-diameter ratio were conducted. Geologic model age estimation was carried out in the mare area that was divided into three geologic units according to the TiO₂ abundance. The result showed that the east part of the mare had the oldest model age of $\mu {3.27}_{-0.045}^{+0.036}$ Ga, and the middle part of the mare had the youngest model age of $\mu {2.49}_{-0.073}^{+0.072}$ Ga. The crater catalogue and the surface model age analysis results were used to support topographic and geologic analyses of the pre-selected landing area of the CE-6 mission before the launch and will contribute to further scientific researches after the lunar samples are returned to Earth.

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.

In planetary rover missions, rover path planning is critical to ensure the safety and efficiency of the rover traverse and in situ explorations. In the Chang’e-4 (CE-4) mission, we have proposed and developed vision-based decision support methods comprising obstacle map generation and path evaluation for the rover. At each waypoint along the rover traverse, digital elevation model (DEM) is automatically generated, which is then used for obstacle map generation and path searching. For path evaluation, the searched path and the predicted wheel trajectories are projected onto the original images captured by different cameras to coincide with the real observation scenario. The proposed methods have been applied in the CE-4 mission to support teleoperation of the rover, and examples of multiple applications used in the mission are presented in this paper. Under the support of the proposed techniques, by the end of the 14th lunar day (3 February 2020), the rover has already travelled 367.25 meters on the far side of the Moon.

In the study of SLAM problem using an RGB-D camera, depth information and visual information as two types of primary measurement data are rarely tightly coupled during refinement of camera pose estimation. In this paper, a new method of RGB-D camera SLAM is proposed based on extended bundle adjustment with integrated 2D and 3D information on the basis of a new projection model. First, the geometric relationship between the image plane coordinates and the depth values is constructed through RGB-D camera calibration. Then, 2D and 3D feature points are automatically extracted and matched between consecutive frames to build a continuous image network. Finally, extended bundle adjustment based on the new projection model, which takes both image and depth measurements into consideration, is applied to the image network for high-precision pose estimation. Field experiments show that the proposed method has a notably better performance than the traditional method, and the experimental results demonstrate the effectiveness of the proposed method in improving localization accuracy.