Mobile context point cloud simplification algorithm based on point feature histogram

Abstract

The invention discloses a mobile context point cloud simplification algorithm based on a point feature histogram (namely a PFH operator). The algorithm mainly comprises the steps of receiving a point cloud set from a point cloud obtaining device; calculating the PFH operator of each point; calculating the standard deviation of the PFH of each point, and designing simplification rules; calculating out a corresponding relation table between the threshold values and the simplification rates of different standard deviations; according to the requirements to the simplification rate from a user, determining the threshold value; reserving the point sets with standard deviations less than or equal to the threshold value in point cloud data, in point cloud data, and eliminating other point sets. According to the mobile context point cloud simplification algorithm based on the point feature histogram, the simplification rate is high, the realtime performance is great, and adequate geometrical characteristics are reserved for scene reconstruction.

Description

technical field [0001] The invention relates to a point cloud simplification algorithm for a mobile scene based on a point feature histogram (English full name is Point Feature Histogram, PFH for short). Background technique [0002] With the development of SLAM (English full name is Simultaneous Localization and Mapping) technology, more and more applications involve the mobile robot carrying imaging equipment to walk in the scene, by processing and analyzing the real-time information obtained by the imaging equipment, to obtain the overall A complete description of the scene, i.e. a 3D reconstruction. Due to the large amount of point cloud data in mobile scenes and high real-time requirements, data reduction before point cloud registration and splicing is very necessary. At the same time, the point cloud data used for 3D reconstruction only needs to retain good geometric features, so that the registered point set can describe the outline of the original model without reta...

AI Technical Summary

Problems solved by technology

Among them, the simplification algorithm based on geometric features is the most common, such as the patent with the publication number 101373540 and the name "point cloud simplification system and method", the publication number 102750730A and the name "a feature-preserving point cloud data reduction method" Patent, Publication No. 102890828A, patent titled "Point Cloud Data Reduction Method Based on Normal Angle", Publication No. 101021954, patent titled "Point Cloud Streamlining Method for 3D Scanning", Publication No. 103701466A, name The patents for "Scattered Point Cloud Compression Algorithm Based on Feature Preservation" are based on normal vector or curvature features for point cloud reduction. Although this type of algorithm is fast and easy to calculate, it only uses a few parameter values ​​​​to approximate a The k-neighborhood geometric features of points cannot obtain too much information, and most scenes contain many feature points, using normal vectors and curvature feature representations, so that these feature points have the same or very similar feature values, the direct result reduces the global feature information

[0009] In addition, the patent with publication number 103065354A and titled "Point Cloud Optimization Method and Its Device" restores and enhances sharp features after preprocessing such as point cloud simplification, so that the optimized point cloud can facilitate the realization of reconstruction technology, but its The calculation is very complicated and cannot meet the timeliness requirements of real-time reconstruction in mobile scenarios

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