This investigation evaluates the performance of digital terrain models (DTMs) generated in different vertical datums by aerial LiDAR and unmanned aerial vehicle (UAV) photogrammetry techniques, for the determination and validation of local geoid models. Many engineering projects require the point heights referring to a physical surface, i.e., geoid, rather than an ellipsoid. When a high-accuracy local geoid model is available in the study area, the physical heights are practically obtained with the transformation of global navigation satellite system (GNSS) ellipsoidal heights of the points. Besides the commonly used geodetic methods, this study introduces a novel approach for the determination and validation of the local geoid surface models using photogrammetry. The numeric tests were carried out in the Bergama region, in the west of Turkey. Using direct georeferenced airborne LiDAR and indirect georeferenced UAV photogrammetry-derived point clouds, DTMs were generated in ellipsoidal and geoidal vertical datums, respectively. After this, the local geoid models were calculated as differences between the generated DTMs. Generated local geoid models in the grid and pointwise formats were tested and compared with the regional gravimetric geoid model (TG03) and a high-resolution global geoid model (EIGEN6C4), respectively. In conclusion, the applied approach provided sufficient performance for modeling and validating the geoid heights with centimeter-level accuracy.

Light detection and ranging (LiDAR) data systems mounted on a moving or stationary platform provide 3D point cloud data for various purposes. In applications where the interested area or object needs to be measured twice or more with a shift, precise registration of the obtained point clouds is crucial for generating a healthy model with the combination of the overlapped point clouds. Automatic registration of the point clouds in the common coordinate system using the iterative closest point (ICP) algorithm or its variants is one of the frequently applied methods in the literature, and a number of studies focus on improving the registration process algorithms for achieving better results. This study proposed and tested a different approach for automatic keypoint detecting and matching in coarse registration of the point clouds before fine registration using the ICP algorithm. In the suggested algorithm, the keypoints were matched considering their geometrical relations expressed by means of the angles and distances among them. Hence, contributing the quality improvement of the 3D model obtained through the fine registration process, which is carried out using the ICP method, was our aim. The performance of the new algorithm was assessed using the root mean square error (RMSE) of the 3D transformation in the rough alignment stage as well as a-prior and a-posterior RMSE values of the ICP algorithm. The new algorithm was also compared with the point feature histogram (PFH) descriptor and matching algorithm, accompanying two commonly used detectors. In result of the comparisons, the superiorities and disadvantages of the suggested algorithm were discussed. The measurements for the datasets employed in the experiments were carried out using scanned data of a 6 cm × 6 cm × 10 cm Aristotle sculpture in the laboratory environment, and a building facade in the outdoor as well as using the publically available Stanford bunny sculpture data. In each case study, the proposed algorithm provided satisfying performance with superior accuracy and less iteration number in the ICP process compared to the other coarse registration methods. From the point clouds where coarse registration has been made with the proposed method, the fine registration accuracies in terms of RMSE values with ICP iterations are calculated as ~0.29 cm for Aristotle and Stanford bunny sculptures, ~2.0 cm for the building facade, respectively.

Bu çalışmada, Apple iPad Pro LiDAR sensörünün tarama ve doğruluk performansı, profesyonel bir Yersel Lazer Tarayıcının (YLT) performansı ile birlikte karşılaştırmalı olarak incelenmiştir. Bu kapsamda, profesyonel bir tarayıcı olan Leica ScanStation C10 ve iki farklı yazılım ile birlikte kullanılan Apple firmasına ait iPad Pro LiDAR sensörü ile, içinde farklı büyüklüklere sahip pek çok objenin yer aldığı bir çalışma ofisi taranmıştır. Yapılan ölçmeler sonrası çalışma ofisinin üç farklı 3-Boyutlu (3B) nokta bulutu üretilmiştir. Ofiste bulunan ve boyutları birkaç cm ile 2 metre arasında değişen bazı objelerin ayrıt uzunlukları bir çelik şerit metre ile mm inceliğinde ölçülmüş ve bu büyüklükler, iki farklı tarayıcıdan üretilen nokta bulutlarından elde edilen değerleri ile karşılaştırılmıştır. Çalışma sonuçları profesyonel bir tarayıcı ile, kapalı küçük bir ortamda, ±0.5 cm karesel ortalama hata ile 3B nokta bulutu üretilebileceğini göstermiştir. iPad Pro LiDAR sensörü kullanılarak üretilen nokta bulutlarının ise, veri toplamada kullanılan yazılıma bağlı olarak, ± 1-1.5 cm karesel ortalama hataya sahip olduğu görülmüştür. Yapılan bu çalışmadan elde edilen ilk sonuçlar, Apple iPad Pro LiDAR sensörünün, kapalı mekanlarda yapılan uygulamalar için ümit verici bir performans sergilediğini, düşük maliyeti, taşınabilirliği, hızı ve kolay kullanılabilirliği gibi unsurlarıyla da yüksek maliyete sahip profesyonel tarayıcılara önemli bir alternatif olabileceğini göstermiştir.

Keywords: Lidar, Laser Scanning, Photogrammetry, Segmentation, K-means, ANN Segmentation of point clouds is recently used in many Geomatics Engineering applications such as the building extraction in urban areas, Digital Terrain Model (DTM) generation and the road or urban furniture extraction. Segmentation is a process of dividing point clouds according to their special characteristic layers. The present paper discusses K-means and self-organizing map (SOM) which is a type of ANN (Artificial Neural Network) segmentation algorithm which treats the segmentation of point cloud. The point clouds which generate with photogrammetric method and Terrestrial Lidar System (TLS) were segmented according to surface normal, intensity and curvature. Thus, the results were evaluated. LIDAR (Light Detection and Ranging) and Photogrammetry are commonly used to obtain point clouds in many remote sensing and geodesy applications. By photogrammetric method or LIDAR method, it is possible to obtain point cloud from terrestrial or airborne systems. In this study, the measurements were made with a Leica C10 laser scanner in LIDAR method. In photogrammetric method, the point cloud was obtained from photographs taken from the ground with a 13 MP non-metric camera.