Modeling method based on point cloud data

Abstract

The invention discloses a modeling method based on point cloud data, and the method comprises the following steps: A), carrying out the laser detection of a to-be-modeled object, and obtaining laser radar scanning data; b) obtaining target data by using a PCA algorithm; c) filtering the target data based on a radius filter in combination with a grid radius filter to obtain filtered data; d) removing burrs in the filtering data by using a convolution model, and extracting a single-pixel skeleton by using a skeleton extraction algorithm; e) calculating the slope of each pixel point on the single-pixel skeleton to obtain single-pixel skeleton sections with different slopes, calculating a distance value between two adjacent end points on the two single-pixel skeleton sections with the same slope or vertical slopes, and comparing the distance value with a set threshold value to connect the two single-pixel skeleton sections when the distance value is smaller than the threshold value; and F) restoring the three-dimensional structure based on the optimized single-pixel skeleton. The method is high in modeling recovery efficiency and high in precision.

Description

technical field [0001] The invention relates to a scanning modeling method, in particular to a modeling method based on point cloud data. Background technique [0002] With the development of smart mobile devices (such as food delivery robots, etc.), more and more smart mobile devices have been involved in indoor use scenarios, and the accuracy of the movement path of smart mobile devices in indoor environments is also high. , so it is necessary to be able to obtain accurate indoor structures. [0003] In recent years, lidar technology has developed rapidly due to its high accuracy, low cost, good portability, and wide application range. With the increasing maturity of indoor navigation technology, accurate building interior structures can be obtained from point cloud data It is of great significance to achieve accurate indoor navigation. [0004] However, it is difficult and time-consuming to extract indoor structures from disordered and high-density point cloud data, and...

AI Technical Summary

Problems solved by technology

[0004] However, it is difficult and time-consuming to extract indoor structures from disordered and high-density point cloud data, and the noise generated by the algorithm and scanning environment greatly increases the complexity of data processing, and the existing technology for point cloud data The extraction of data, or the large amount of data processing is inefficient, or it will cause data loss. It is difficult to realize the extraction of lidar scanning data and the restoration of indoor structures in a timely and accurate manner.

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