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Article

Robust Multi-Sensor Point Cloud Registration for Cultural Heritage Documentation: A Multi-Population Based Differential Evolution Approach

by
Ahmet Emin Karkınlı
1,
Artur Janowski
2,*,
Leyla Kaderli
3,
Betül Gül Hüsrevoğlu
4 and
Mustafa Hüsrevoğlu
1
1
Department of Geomatics Engineering, Faculty of Engineering, Niğde Ömer Halisdemir University, 51240 Niğde, Türkiye
2
Department of Geodesy, Institute of Geodesy and Civil Engineering, Faculty of Geoengineering, University of Warmia and Mazury in Olsztyn, Oczapowskiego 1, 10-719 Olsztyn, Poland
3
Department of Architecture, Faculty of Architecture, Erciyes University, 38280 Kayseri, Türkiye
4
Department of Architecture, Graduate School of Natural and Applied Sciences, Erciyes University, 38280 Kayseri, Türkiye
*
Author to whom correspondence should be addressed.
Remote Sens. 2026, 18(12), 1971; https://doi.org/10.3390/rs18121971 (registering DOI)
Submission received: 16 April 2026 / Revised: 1 June 2026 / Accepted: 11 June 2026 / Published: 13 June 2026

Abstract

The digital preservation of built cultural heritage requires precise documentation techniques capable of capturing complex architectural geometries often affected by occlusions and data voids. This study presents a robust multi-sensor fusion workflow integrating Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle (UAV) photogrammetry for the 3D reconstruction of the Hasaköy (Sasima) Church in Niğde, Türkiye. To address the limitations of traditional registration methods, specifically the susceptibility of the Iterative Closest Point (ICP) algorithm to local minima in datasets with partial overlaps, this study proposes a fine-tuning approach based on the Multi-population Based Differential Evolution (MDE) algorithm. The methodology employs a coarse-to-fine strategy, initiating with Fast Point Feature Histogram (FPFH) extraction and RANSAC (Random Sample Consensus) for global alignment, followed by TR-ICP, MDE, PSO, and Aquila Optimizer (AO) evaluation, computational-time analysis, FPFH-radius sensitivity testing, and 6-DoF transformation decomposition to characterize both accuracy and operational cost. In the 30-run fine-tuning evaluation, MDE reduced the mean bidirectional trimmed RMSE from 0.4152 m for TR-ICP to 0.3726 m. With a population parameter of 10, MDE retained a low median RMSE of 0.3718 m, while PSO exhibited a wider stochastic tail under the same bounded 6-DoF search budget. AO produced a higher mean bidirectional trimmed RMSE of 0.5233 m. The decimeter-scale bidirectional RMSE should be interpreted as a cross-source, partial-overlap distance metric rather than sensor precision; the overlapping facade objective was approximately 2.4–2.8 cm, and the UAV block was independently controlled with a 1.34 cm GCP RMSE. This study establishes a transparent and reproducible framework for heritage documentation, supporting the faithful digital preservation of endangered monuments with complex typologies.
Keywords: cultural heritage documentation; multi-sensor fusion; point cloud registration; heuristic optimization; terrestrial laser scanning; UAV photogrammetry; multi-population based differential evolution cultural heritage documentation; multi-sensor fusion; point cloud registration; heuristic optimization; terrestrial laser scanning; UAV photogrammetry; multi-population based differential evolution

Share and Cite

MDPI and ACS Style

Karkınlı, A.E.; Janowski, A.; Kaderli, L.; Hüsrevoğlu, B.G.; Hüsrevoğlu, M. Robust Multi-Sensor Point Cloud Registration for Cultural Heritage Documentation: A Multi-Population Based Differential Evolution Approach. Remote Sens. 2026, 18, 1971. https://doi.org/10.3390/rs18121971

AMA Style

Karkınlı AE, Janowski A, Kaderli L, Hüsrevoğlu BG, Hüsrevoğlu M. Robust Multi-Sensor Point Cloud Registration for Cultural Heritage Documentation: A Multi-Population Based Differential Evolution Approach. Remote Sensing. 2026; 18(12):1971. https://doi.org/10.3390/rs18121971

Chicago/Turabian Style

Karkınlı, Ahmet Emin, Artur Janowski, Leyla Kaderli, Betül Gül Hüsrevoğlu, and Mustafa Hüsrevoğlu. 2026. "Robust Multi-Sensor Point Cloud Registration for Cultural Heritage Documentation: A Multi-Population Based Differential Evolution Approach" Remote Sensing 18, no. 12: 1971. https://doi.org/10.3390/rs18121971

APA Style

Karkınlı, A. E., Janowski, A., Kaderli, L., Hüsrevoğlu, B. G., & Hüsrevoğlu, M. (2026). Robust Multi-Sensor Point Cloud Registration for Cultural Heritage Documentation: A Multi-Population Based Differential Evolution Approach. Remote Sensing, 18(12), 1971. https://doi.org/10.3390/rs18121971

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