Point cloud data construction based on VSLAM image processing integrating CNN and transformer

With the widespread application of smart devices in daily life and industry, efficient environmental perception and understanding have become a crucial research focus. Visual Simultaneous Localization and Mapping (VSLAM) technology, as a vision-based positioning and mapping method, has gradually emerged as a hub for research in the area of smart devices due to its real-time performance, high precision, autonomy, and other advantages. This trend reflects the growing demand for advanced perception and localization capabilities in the development of intelligent systems. This article is based on VSLAM technology, acquiring images of the environment through sensors using visual image capture, and using convolutional neural network (CNN) and Transformer to process the image and extract point cloud data. Then, the data is optimized through point cloud algorithms to achieve efficient perception and understanding of environmental information. This article will introduce in detail the combination of the processing of images in binocular cameras with CNN and Visual Transformer to give full play to their respective advantages to provide richer feature information and improve the accuracy and processing speed of image feature points. The processed images can be reconstructed in 3D by the binocular camera, using different point cloud technologies such as point cloud filtering, registration, and deep learning to realize synchronous real-time positioning and mapping.