Array point cloud real-time outlier removal method and system based on sliding completion

By using the sliding completion method, combined with hierarchical outlier noise removal and array discrete probability model, the adaptability and accuracy issues of noise processing in sparse point clouds of SPAD array lidar are solved, and efficient noise removal of sparse point clouds is achieved.

CN119251084BActive Publication Date: 2026-07-03NANJING UNIV OF INFORMATION SCI & TECH
View PDF 2 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
NANJING UNIV OF INFORMATION SCI & TECH
Filing Date
2024-09-19
Publication Date
2026-07-03

AI Technical Summary

Technical Problem

Existing technologies cannot effectively remove outlier noise from sparse point clouds of SPAD array lidar, and existing algorithms have difficulty capturing the feature differences between noise and real points in sparse point clouds, resulting in poor adaptability and low denoising accuracy.

Method used

A real-time outlier noise removal method for array point clouds based on sliding completion is adopted. By using hierarchical outlier noise removal and array discrete probability model completion strategy, combined with sliding window dynamic update, high-precision denoising of sparse point clouds can be achieved.

Benefits of technology

While ensuring real-time performance, high-precision denoising of array sparse point clouds was achieved, overcoming the problems of weak adaptability and low denoising accuracy in existing technologies.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN119251084B_ABST
    Figure CN119251084B_ABST
Patent Text Reader

Abstract

The application provides a kind of array point cloud real-time outlier removal method and system based on sliding completion, it is related to point cloud outlier processing field.The initial input multiple frame array point cloud is preprocessed, incomplete point cloud is generated and stored in sliding window;Incomplete point cloud of each frame of subsequent input is obtained, the removed point is regarded as the feature point of current frame, and the union of point cloud in sliding window is regarded as the point cloud to be filled;A completion strategy based on array discrete probability model is designed to screen the feature points;The screened feature points are filled into the incomplete point cloud of current frame, which is the denoised point cloud of current frame, and the denoising of one frame of point cloud is completed;After the denoising of current frame point cloud is completed, the corresponding incomplete point cloud is added to the tail of sliding window, and the point cloud at the head of sliding window is removed, to realize the dynamic update of sliding window.The application overcomes the problem that the prior art has weak adaptability and low denoising precision when processing array point cloud outliers.
Need to check novelty before this filing date? Find Prior Art