When laser scanning is performed in some indoor environment, because of the mirror reflection problem, the exact information of the scene cannot be restored. In this paper, a method to detect the mirror based on mirror symmetry principle is proposed. By finding the corresponding relationship between the actual object and its image in the mirror, the position of the mirror could be determined without integrating any other sensor information.

Pneumothorax is a common injury in disaster rescue, traffic accidents, and war trauma environments and requires early diagnosis and treatment. The commonly used X-ray, CT, and other diagnostic instruments are not suitable for rescue sites due to their large size, heavy weight, and difficulty in transportation. Ultrasound equipment is easy to carry and suitable for rescue environments. However, ultrasound images are noisy, have low resolution, and are difficult to get started, which affects the efficiency of diagnosis. This paper studies the effect of lung ultrasound image recognition and classification based on compressed sensing and BP neural network. We use ultrasound equipment to build a lung simulation model, collect five typical features of lung ultrasound images in M-mode, and build a dataset. Using compressed sensing theory, we design sparse matrix and observation matrix and perform data compression on the image data in the dataset to obtain observation values. We design a BP neural network, input the observations into the network for training, and compare it with the commonly used VGG16 network. The method proposed in this paper has higher recognition accuracy and significantly fewer parameters than VGG16, so it is suitable for use in embedded devices.

Binocular stereo vision is based on the principle of parallax and uses imaging equipment to obtain two images of the measured object from different positions, and obtains the three-dimensional geometric information of the object by calculating the position deviation between the corresponding points of the image. As an important execution component of the six-degree-of-freedom platform in the flight simulator, the electric cylinder can undertake the important task of dynamic simulation, and its abnormal working state directly affects the training effect and life safety of the flight simulator users. In this paper, the electric cylinder position pose is obtained by the three-dimensional point cloud method, and the improved active learning anomaly detection algorithm is proposed to predict the electric cylinder position posture, so as to determine whether the platform electric cylinder fails.

The ORB-SLAM3 algorithm operates around the matching relationship of feature points. Only when the extracted matching points are sufficient and accurate can the camera pose and the world coordinates of map points be calculated correctly and quickly, then sufficient effective data can be extracted in the environment. It is particularly important. ORB-SLAM3 can extract images up to 30ms/frame. The real-time performance on the PC is very good, but the performance on the embedded side is not good enough. This paper optimizes the problems existing in the feature point extraction and map construction of the monocular ORB-SLAM3 system to improve the illumination invariance, uniformity and human-computer interaction of the algorithm, improve the threshold and improve the extraction accuracy with the help of the IMU sensor. The radius algorithm and YOLOv7 algorithm filter the best points to improve the matching accuracy of feature points. In order to achieve the final effect of augmented reality, a set of augmented reality development system was constructed based on the Unity3D cross-platform game engine. The system guarantees synchronous rendering, particle effects, background video rendering and real-time shadow rendering. At the same time, the interface is packaged and can be connected to the perception positioning algorithm library under different platforms. The augmented reality system is completed by using HoloLens. The system can autonomously detect the environment, Track and record the position of relevant objects in the environment, and at the same time use the visual SLAM perception positioning algorithm to ensure the safety of users, providing a new idea for the application of augmented reality devices.

At present, emotion recognition has become a research hotspot in the field of pattern recognition. Considering the problems of incomplete information and strong interference in single‐modal emotion recognition, multimodal emotion recognition has been widely studied. Multimodal data includes, but is not limited to, emoji, text, and voice modality data. There are various ways to express emotion, among which expression, text and voice are the most direct and reliable emotional information carriers. Therefore, it is of great research and practical significance to comprehensively consider the emotion recognition research of expression, text and voice modalities, and to apply its research results to the field of virtual reality (referred to as VR).This paper analyzes the relevant situation of multimodal emotion recognition, extracts features of voice, text and expression, and then fuses them into multimodal for emotional analysis, and applies it to the VR field. The main work content is as follows: the relevant technologies of multimodal emotion recognition research in the field of VR are introduced, including deep learning related technologies, virtual reality technology, and multimodal fusion methods. In terms of deep learning, the focus is on convolutional neural networks and recurrent neural networks and their variants. In terms of virtual reality technology, the characteristics and applications of virtual reality are introduced. In terms of multimodal fusion, three commonly used fusion methods are introduced.