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

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.