The invention relates to a method for realizing a visual SLAM semantic mapping function based on a cavity convolutional deep neural network. The method comprises the following steps of (1) using an embedded development processor to obtain the color information and the depth information of the current environment via a RGB-D camera; (2) obtaining a feature point matching pair through the collectedimage, carrying out pose estimation, and obtaining scene space point cloud data; (3) carrying out pixel-level semantic segmentation on the image by utilizing deep learning, and enabling spatial pointsto have semantic annotation information through mapping of an image coordinate system and a world coordinate system; (4) eliminating the errors caused by optimized semantic segmentation through manifold clustering; and (5) performing semantic mapping, and splicing the spatial point clouds to obtain a point cloud semantic map composed of dense discrete points. The invention also relates to a system for realizing the visual SLAM semantic mapping function based on the cavity convolutional deep neural network. With the adoption of the method and the system, the spatial network map has higher-level semantic information and better meets the use requirements in the real-time mapping process.
A method and system for realizing a visual SLAM semantic mapping function based on a cavity convolutional deep neural network