Application Analysis of Deep Learning in Visual SLAM Front-end
The analysis was conducted focusing on the application of deep learning in the front-end of visual SLAM,aiming to provide beneficial insights for future applications of deep learning in this field.Firstly,visual SLAM was introduced.Subsequently,the application of deep learning in the front-end of visual SLAM was elaborated in detail,including preprocessing,feature extraction,data association,and pose optimization.Then,an in-depth analysis was carried out on the advantages and disadvantages of deep learning compared to traditional techniques,as well as the challenges faced in its application in the front-end of visual SLAM.Finally,predictions were made about future development trends,including the design of lightweight network structures,feature extraction,data association methods,the evolution of end-to-end training modes,and the application of multimodal information fusion.
万方数据
维普
