Point Clouds Outlier Removal Method Based on Improved Mahalanobis and Completion-Reference-Cited by-同舟云学术

Point Clouds Outlier Removal Method Based on Improved Mahalanobis and Completion

Published:2023-01 Issue:1 Volume:8 Page:17-24
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Qu Chengzhi¹^ORCID,Zhang Yan¹^ORCID,Huang Kun¹,Wang Shuang¹,Yang Yang²^ORCID

Affiliation:

1. School of Aeronautics and Astronautics, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China

2. School of Mechanical and Manufacturing Engineering, the University of New South Wales, Sydney, NSW, Australia

Funder

Shenzhen Science and Technology Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/7083369/9963768/09944850.pdf?arnumber=9944850

Reference25 articles.

1. Fast Memory Efficient Local Outlier Detection in Data Streams

2. An efficient outlier removal method for scattered point cloud data

3. Optimization Algorithm for Point Cloud Quality Enhancement Based on Statistical Filtering

4. Non-iterative denoising algorithm based on a dual threshold for a 3D point cloud

5. Clustering With Outlier Removal

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