Coupled indoor three-dimensional semantic mapping and modeling method

Abstract

The invention discloses a coupled indoor three-dimensional semantic mapping and modeling method and a medium. The method comprises the steps of obtaining initial point cloud data, extracting feature points, estimating poses corresponding to the initial data frames and generating a local point cloud map according to the poses, generating a training data set, performing semantic annotation on the local point cloud map based on the deep neural network and the training data set, and feeding back a semantic annotation result of the local point cloud map to the initial data frame, optimizing the pose to obtain a first pose optimization result, extracting a semantic structure surface, and associating the semantic structure surface with the global plane, carrying out nonlinear optimization to obtain a second pose optimization result, and generating a final point cloud map, a semantic point cloud map and a building internal semantic line structure model. A semantic point cloud map and an internal wireframe structure model of an indoor environment can be accurately reconstructed; and meanwhile, the situation that pose estimation is lost due to violent movement of the acquisition platform during laser-based mobile scanning is prevented.

Description

technical field [0001] The invention relates to the technical field of indoor three-dimensional digitization, in particular to a coupled indoor three-dimensional semantic mapping and modeling method and a computer-readable storage medium. Background technique [0002] With the advent of the era of smart cities, there is an increasing need to acquire and update digital information of an increasing number of large buildings. [0003] In related technologies, the digitization of large-scale indoor environments is mostly measured by sensors based on vision, laser or structured light ranging; however, in the existing measurement methods, the reconstructed point cloud map has no semantic information and needs to be followed up. Semantic segmentation, this work will consume a lot of manpower and material resources; and, when performing laser-based mobile scanning, it is easy to lose the pose estimation due to the violent movement of the acquisition platform. Contents of the inven...

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Problems solved by technology

[0003] In related technologies, the digitization of large-scale indoor environments is mostly measured by sensors based on vision, laser or structured light ranging; however, in the existing measurement methods, the reconstructed point cloud map has no semantic information and needs to be followed up. Semantic segmentation, this work will consume a lot of manpower and material resources; and, when performing laser-based mobile scanning, it is easy to lose the pose estimation due to the violent movement of the acquisition platform

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