For this study, a new point cloud alignment method is proposed for extracting corner points and aligning them at the geometric level. It can align point clouds that have low overlap and is more robust to outliers and noise. First, planes are extracted from the raw point cloud, and the corner points are defined as the intersection of three planes. Next, graphs are constructed for subsequent point cloud registration by treating corners as vertices and sharing planes as edges. The graph-matching algorithm is then applied to determine correspondence. Finally, point clouds are registered by aligning the corresponding corner points. The proposed method was investigated by utilizing pertinent metrics on datasets with differing overlap. The results demonstrate that the proposed method can align point clouds that have low overlap, yielding an RMSE of about 0.05 cm for datasets with 90% overlap and about 0.2 cm when there is only about 10% overlap. In this situation, the other methods failed to align point clouds. In terms of time consumption, the proposed method can process a point cloud comprising ${10}^4$ points in 4 s when there is high overlap. When there is low overlap, it can also process a point cloud comprising ${10}^6$ points in 10 s. The contributions of this study are the definition and extraction of corner points at the geometric level, followed by the use of these corner points to register point clouds. This approach can be directly used for low-precision applications and, in addition, for coarse registration in high-precision applications.

Simultaneous localization and mapping (SLAM) plays a crucial role in the field of intelligent mobile robots. However, the traditional Visual SLAM (VSLAM) framework is based on strong assumptions about static environments, which are not applicable to dynamic real-world environments. The correctness of re-localization and recall of loop closure detection are both lower when the mobile robot loses frames in a dynamic environment. Thus, in this paper, the re-localization and loop closure detection method with a semantic topology graph based on ORB-SLAM2 is proposed. First, we use YOLOv5 for object detection and label the recognized dynamic and static objects. Secondly, the topology graph is constructed using the position information of static objects in space. Then, we propose a weight expression for the topology graph to calculate the similarity of topology in different keyframes. Finally, the re-localization and loop closure detection are determined based on the value of topology similarity. Experiments on public datasets show that the semantic topology graph is effective in improving the correct rate of re-localization and the accuracy of loop closure detection in a dynamic environment.

Visual simultaneous localization and mapping (VSLAM) is pivotal for intelligent mobile robots. VSLAM systems can be used to identify scenes by obtaining massive amounts of redundant texture information from the environment. However, VSLAM faces a major challenge in dynamic low-light environments, in which the extraction of feature points is often difficult, leading to tracking failure with mobile robots. Therefore, we developed a method to improve the feature point extraction method used for VSLAM. We first used the contrast limited adaptive histogram equalization (CLAHE) method to increase the contrast in low-light images, allowing for the extraction of more feature points. Second, in order to increase the effectiveness of the extracted feature points, the redundant feature points were removed. We developed three conditions to filter the feature points. Finally, the proposed method was tested on popular datasets (e.g., TUM and OpenLORIS-Scene), and the results were compared with those of several traditional methods. The results of the experiments showed that the proposed method is feasible and highly robust in dynamic low-light environments.

In the realm of industrial quality control, visual inspection plays a pivotal role in ensuring product precision and consistency. Moreover, it enables non-contact inspection, preventing the products from potential damage, and timely monitoring capabilities facilitate quick decision making. However, traditional methods, such as manual inspection using feeler gauges, are time-consuming, labor-intensive, and prone to human error. To address these limitations, this study proposes a deep learning-based visual inspection system for measuring gap spacing in high-precision equipment. Utilizing the DeepLSD algorithm, the system integrates traditional and deep learning techniques to enhance line segment detection, resulting in more robust and accurate inspection outcomes. Key performance improvements were realized, with the proposed system being a piece of deep learning-enabled high-precision mobile equipment for inspecting gap spacing in real-time. Through a comparative analysis with the traditional feeler gauge method, the proposed system demonstrated significant improvements in inspection time, accuracy, and user experience, while reducing workload. Experimental results validate the effectiveness and efficiency of the proposed approach, highlighting its potential for widespread application in industrial quality inspection activities.

Extended Reality (XR)—encompassing Virtual Reality (VR), Mixed Reality (MR), and Augmented Reality (AR)—opens transformative horizons for revolutionizing remote collaborative systems in various contexts. This review presents a multidimensional synthesis of cutting-edge developments and rigorous technical analysis for XR remote collaboration using sketch cues. We systematically investigate the potential of using sketch cues in XR remote collaboration from local/remote interface design, interaction techniques, visual communication cues, and system evaluation protocols. We analyze in depth the transformative impact of sketch-driven XR remote collaboration, exploring its operational feasibility and strategic value across diverse application domains. This work seeks to establish a pioneering roadmap for future research on sketch-based human–computer interaction and collaborative systems design of XR remote collaboration, offering theoretical insights and practical guidance for advancing this rapidly evolving field.

We provide a literature review of XR remote collaboration supporting visual annotation cues for industry.