The application belongs to the technical field of
point cloud processing, and discloses a multi-
modal semantic mapping method and
system based on a master-slave architecture. The method comprises the following steps: S1, extracting feature points in
point cloud data and calculating the
covariance matrix of the neighborhood
point cloud of each feature point; S2, solving the optimal
pose corresponding to the minimum of the target function; S3, converting the obtained point
cloud data into a world coordinate
system using the optimal
pose, fusing all frame point
cloud data in the world coordinate
system to form a three-dimensional point cloud map, projecting the three-dimensional point cloud map to a camera coordinate system, and inputting the three-dimensional point cloud map projected to the camera coordinate system into a semantic segmentation neural network to obtain the semantic
label corresponding to each point cloud in the three-dimensional point cloud map, thereby achieving
semantic mapping. Through the application, the problems of weak dynamic adaptability, insufficient model generalization ability and long-term running precision decay of the traditional three-dimensional
semantic mapping method in a dynamic complex scene are solved.