Tianwen-1 (TW-1) is the first Chinese interplanetary mission to have accomplished orbiting, landing, and patrolling in a single exploration of Mars. After safe landing, it is essential to reconstruct the descent trajectory and determine the landing site of the lander. For this purpose, we processed descent images of the TW-1 optical obstacle-avoidance sensor (OOAS) and digital orthophoto map (DOM) of the landing area using our proposed hybrid-matching method, in which the landing process is divided into two parts. In the first, crater matching is used to obtain the geometric transformations between the OOAS images and DOM to calculate the position of the lander. In the second, feature matching is applied to compute the position of the lander. We calculated the landing site of TW-1 to be 109.9259° E, 25.0659° N with a positional accuracy of 1.56 m and reconstructed the landing trajectory with a horizontal root mean squared error of 1.79 m. These results will facilitate the analyses of the obstacle-avoidance system and optimize the control strategy in the follow-up planetary-exploration missions.

Printed circuit board (PCB) defect detection is critical for ensuring the safety of electronic devices, especially in the space industry. Non-reference-based methods, typically the deep learning methods, suffer from a large amount of annotated data requirements and poor interpretability. In contrast, conventional reference-based methods achieve higher detection accuracy by comparing with a template image but rely on precise image alignment and face the challenge of fine defects detection. To solve the problem, we propose a novel Edge-guided Energy-based PCB Defect Detection method (EEDD). We focus on the salient edge characteristic of PCB images and regard the functional defects as contour differences and then propose a novel energy measurement method for PCB contour anomaly detection. We introduce the energy transformation using the edge information of the template and test image, then Speeded-Up Robust Features method (SURF) is used for image alignment, and finally achieve defect detection by measuring the energy anomaly score pixel by pixel with the proposed energy-based defect localization and contour flood fill methods. Our method excels in detecting multi-scale defects, particularly tiny defects, and is robust against interferences such as non-finely aligned images and edge spurs. Experiments on the DeepPCB-A dataset and our high-resolution PCB dataset (HDPCB) show that the proposed method outperforms state-of-the-art methods in PCB defect-detection tasks.