{"title":"Nonlinear least-squares solutions to the TLS multi-station registration adjustment problem","authors":"","doi":"10.1016/j.isprsjprs.2024.09.014","DOIUrl":null,"url":null,"abstract":"<div><p>Performing multiple scans is necessary to cover an entire scene of interest, making multi-station registration adjustment a critical task in terrestrial laser scanner data processing. Existing methods either rely on pair-wise adjustment, which leads to drift accumulation and lacks global consistency, or provide an approximate solution based on a linearized model, sacrificing statistical optimality. In this study, using a multi-station stacking model, we propose a method that provides two different nonlinear least-squares (LS) solutions to this problem. We first demonstrate how a nonlinear Baarda’s S-transformation can be used to transform solutions that share the same optimal network configuration. Then, two practically meaningful LS solutions are introduced, i.e., the trivial minimal-constraints solution and the partial nearest solution. Most importantly, we derive a truncated Gauss–Newton iterative scheme to obtain numerically exact solutions to the corresponding nonlinear rank-deficient problem. We validate our method with three real-world examples, demonstrating that (1) global consistency is maintained with no drift accumulation, and (2) our nonlinear solution outperforms the approximate linearized solution. Code and data are available at <span><span>https://github.com/huyuchn/Multi-station-registration</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":10.6000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISPRS Journal of Photogrammetry and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0924271624003526","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Performing multiple scans is necessary to cover an entire scene of interest, making multi-station registration adjustment a critical task in terrestrial laser scanner data processing. Existing methods either rely on pair-wise adjustment, which leads to drift accumulation and lacks global consistency, or provide an approximate solution based on a linearized model, sacrificing statistical optimality. In this study, using a multi-station stacking model, we propose a method that provides two different nonlinear least-squares (LS) solutions to this problem. We first demonstrate how a nonlinear Baarda’s S-transformation can be used to transform solutions that share the same optimal network configuration. Then, two practically meaningful LS solutions are introduced, i.e., the trivial minimal-constraints solution and the partial nearest solution. Most importantly, we derive a truncated Gauss–Newton iterative scheme to obtain numerically exact solutions to the corresponding nonlinear rank-deficient problem. We validate our method with three real-world examples, demonstrating that (1) global consistency is maintained with no drift accumulation, and (2) our nonlinear solution outperforms the approximate linearized solution. Code and data are available at https://github.com/huyuchn/Multi-station-registration.
期刊介绍:
The ISPRS Journal of Photogrammetry and Remote Sensing (P&RS) serves as the official journal of the International Society for Photogrammetry and Remote Sensing (ISPRS). It acts as a platform for scientists and professionals worldwide who are involved in various disciplines that utilize photogrammetry, remote sensing, spatial information systems, computer vision, and related fields. The journal aims to facilitate communication and dissemination of advancements in these disciplines, while also acting as a comprehensive source of reference and archive.
P&RS endeavors to publish high-quality, peer-reviewed research papers that are preferably original and have not been published before. These papers can cover scientific/research, technological development, or application/practical aspects. Additionally, the journal welcomes papers that are based on presentations from ISPRS meetings, as long as they are considered significant contributions to the aforementioned fields.
In particular, P&RS encourages the submission of papers that are of broad scientific interest, showcase innovative applications (especially in emerging fields), have an interdisciplinary focus, discuss topics that have received limited attention in P&RS or related journals, or explore new directions in scientific or professional realms. It is preferred that theoretical papers include practical applications, while papers focusing on systems and applications should include a theoretical background.