Visual-based simultaneous localization and mapping (SLAM) systems perform weakly in object tracking and map reconstruction due to the unreliable depth measurement originating from image-only data. Light Detection and Ranging (LiDAR) can be coupled to overcome the drawback of uncertain depth estimation. The prerequisite for performing data fusion is to align visual-Lidar sensors to a specific coordinate system with extrinsic pose by calibrating. The conventional extrinsic calibration frameworks either rely on markers in artificial large-size calibration boards or uncontrollable natural scenes (Fig. 2 ), limiting stability and convenience. In this paper, we have designed a novel marker pattern, A4LidarTag, composed of circular holes. The difference in depth measurement is used to encode location information. Based on A4LidarTag, the automatic extrinsic calibration framework between solid-state Lidar (SSL) and the camera is developed. The proposed framework can be implemented in close range (within 1 m) and on an A4-size calibration board. The average reprojection error resulting from Lidar point clouds projection is about 0.12pixels. Experiments show excellent efficiency and versatility in both indoor and outdoor scenes.

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.

At present, in the large-scale book management scene, book sorting, daily maintenance and book retrieval are very common, but the book information is complicated and the efficiency of relying on manual management is extremely poor. Although there have been many self-service book systems based on optics or vision, they are mostly based on traditional computer vision algorithms such as boundary extraction. Due to the fact that there are more artificial experience thresholds, some shortcomings such as low detection accuracy, poor robustness, and inability to systematically deploy on a large scale, which lack of insufficient intelligence. Therefore, we proposed a book information extraction algorithm based on object detection and optical character recognition (OCR) that is suitable for multiple book information recognition, multiple book image angles and multiple book postures. It can be applied to scenes such as book sorting, bookshelf management and book retrieval. The system we designed includes the classification of book covers and back covers, the classification of books upright and inverted, the detection of book pages side and spine side, the recognition of book pricing. In terms of accuracy, the classification accuracy of the front cover and the back cover is 99.9%, the upright classification accuracy of book front covers is 98.8%, the back cover reaches 99.9%, the accuracy of book price recognition get 94.5%, and the book spine/page side detection mAP reaches 99.6%; in terms of detection speed, Yolov5 detection model was improved and the statistical-based pre-pruning strategy was adopted, support by our algorithm the system reaches 2.09 FPS in book price recognition, which improves the detection speed to meet actual needs.