Robust and Fast Point Cloud Registration for Robot Localization Based on DBSCAN Clustering and Adaptive Segmentation

Robust and Fast Point Cloud Registration for Robot Localization Based on DBSCAN Clustering and Adaptive Segmentation

This paper proposes a registration approach rooted in point cloud clustering and segmentation, named Clustering and Segmentation Normal Distribution Transform (CSNDT), with the aim of improving the scope and efficiency of point cloud registration. Traditional Normal Distribution Transform (NDT) algo...

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Bibliographic Details
Main Authors: Haibin Liu, Yanglei Tang, Huanjie Wang
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Sensors
Subjects:
robot localization
point cloud registration
normal distribution transform (NDT)
density-based spatial clustering of applications with noise (DBSCAN)
clustering and segmentation
Online Access:https://www.mdpi.com/1424-8220/24/24/7889
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