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<p>The increasing popularity and availability of smartphones integrated with high-precision sensors have paved the way for innovative scientific applications and cost-effective observations. In the field of geodetics, the Light Detection and Ranging (LiDAR) sensor plays a crucial role, facilitating the rapid acquisition of three-dimensional point clouds that represent objects. This capability is especially advantageous when complementing measurements obtained using terrestrial laser scanning (TLS), which represents a ground-based LiDAR technology. The use of a LiDAR-equipped smartphone enables the efficient capture of small fragments, thereby eliminating the necessity for an expensive, typically of considerable size, and not always freely accessible terrestrial scanner. However, it is imperative to register the point clouds acquired with a smartphone’s LiDAR sensor with those obtained using a terrestrial scanner during the pre-processing stage. The objective of this study is to propose a novel method for point cloud registration. Dedicated spatial measurement targets (SMTs) were incorporated into the Iterative Closest Point (ICP) algorithm for the purpose of enhancing the registration process. The efficacy of the proposed method was evaluated through a series of tests involving measurements obtained with a LiDAR-equipped smartphone, with and without the incorporation of SMTs. The results were further compared to measurements conducted using TLS. The analysis demonstrated that the newly proposed registration method yielded superior results in comparison to traditional point cloud registration techniques. The integration of SMTs with the ICP algorithm enhances the accuracy and efficiency of point cloud registration, ensuring precise alignment of data from different devices. This method automates the process, reducing manual corrections and expediting data processing. It is particularly beneficial in applications like architecture and construction, where seamless integration of smartphone LiDAR and TLS data improves workflow and reduces costs.</p>

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Reference SMT-assisted ICP approach for integrating low-cost LiDAR and terrestrial laser scanner point clouds

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月8日

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  • Wioleta Błaszczak-Bąk,
  • Tomasz Kozakiewicz

摘要

The increasing popularity and availability of smartphones integrated with high-precision sensors have paved the way for innovative scientific applications and cost-effective observations. In the field of geodetics, the Light Detection and Ranging (LiDAR) sensor plays a crucial role, facilitating the rapid acquisition of three-dimensional point clouds that represent objects. This capability is especially advantageous when complementing measurements obtained using terrestrial laser scanning (TLS), which represents a ground-based LiDAR technology. The use of a LiDAR-equipped smartphone enables the efficient capture of small fragments, thereby eliminating the necessity for an expensive, typically of considerable size, and not always freely accessible terrestrial scanner. However, it is imperative to register the point clouds acquired with a smartphone’s LiDAR sensor with those obtained using a terrestrial scanner during the pre-processing stage. The objective of this study is to propose a novel method for point cloud registration. Dedicated spatial measurement targets (SMTs) were incorporated into the Iterative Closest Point (ICP) algorithm for the purpose of enhancing the registration process. The efficacy of the proposed method was evaluated through a series of tests involving measurements obtained with a LiDAR-equipped smartphone, with and without the incorporation of SMTs. The results were further compared to measurements conducted using TLS. The analysis demonstrated that the newly proposed registration method yielded superior results in comparison to traditional point cloud registration techniques. The integration of SMTs with the ICP algorithm enhances the accuracy and efficiency of point cloud registration, ensuring precise alignment of data from different devices. This method automates the process, reducing manual corrections and expediting data processing. It is particularly beneficial in applications like architecture and construction, where seamless integration of smartphone LiDAR and TLS data improves workflow and reduces costs.