Indoor laser point cloud registration method with enhanced geometric features of neighborhood point pairs

Hanghang Ding; Deli Zhu; Zehui Ren

doi:10.1117/12.3035100

19 July 2024 Indoor laser point cloud registration method with enhanced geometric features of neighborhood point pairs

Hanghang Ding, Deli Zhu, Zehui Ren

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132131H (2024) https://doi.org/10.1117/12.3035100
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

Aiming at the problem of low registration efficiency when indoor home robots face complex scenes and low overlap, a PPFECA-Predator indoor laser point cloud registration network that enhances the geometric features of neighborhood points is proposed. First, the point cloud is sent to the residual block composed of core point convolution KPConv to extract features and sent to the geometric encoding module designed in this article to enrich the local geometric feature information between point pairs; secondly, in the overlapping attention module, the graph After the feature fusion of the convolutional network, the attention ECANet module is added to focus on learning the geometric topology information that makes up the graph; finally, the PPF geometric encoding module and the improved graph convolution module are cascaded decoding operations to form a new geometric information enhancement module GEM,enhances the network’s capture of geometric information. The experimental results show that, by analogy with the benchmark network, on the indoor data set 3dmatch, the registration recall rates are increased to 92.2% and 72.14% respectively, effectively handling registration tasks in complex scenarios such as prominent geometric shapes and low overlap.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hanghang Ding, Deli Zhu, and Zehui Ren "Indoor laser point cloud registration method with enhanced geometric features of neighborhood point pairs", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132131H (19 July 2024); https://doi.org/10.1117/12.3035100

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KEYWORDS

Point clouds

Convolution

Performance modeling

Robots

Deep learning

Computer vision technology

Education and training

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