Photogrammetric and Lidar Data Registration Using Linear Features

Habib, Ayman; Ghanma, Mwafag; Morgan, Michel; Al‐Ruzouq, Rami

doi:10.14358/pers.71.6.699

Cited by 199 publications

(126 citation statements)

References 6 publications

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“…One of the appealing features in the LiDAR output is the direct availability of threedimensional coordinates of points in object space (Habib et al, 2005). The fields of application for LiDAR are very diverse and include generation of digital elevation models (DEMs), 3D-city modelling, forestry management, coastline protection, disaster management, erosion studies, archaeology, monitoring of corridors such as power lines, pipelines, railways and roads.…”

Section: Figure 1 Conception Of Airborne Laser Scanningmentioning

confidence: 99%

Point Distribution as True Quality of LiDAR Point Cloud

Kodors¹

2017

BJMC

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Abstract.A parameter "point density" is often used to evaluate the quality of aerial laser scanning data. It is a parameter simple for understanding and human imagination. However, the true quality of LiDAR point cloud is based on point distribution. There are researches, which mention importance of point distribution and users' false perception, that higher point density is better quality of LiDAR point cloud. The goal of this study is to define the mathematical model how to measure quality of LiDAR point cloud. This article discusses the point distribution and LiDAR data quality defining the image resolution of point cloud. It can be interesting for experts in civil geospatial intelligence, LiDAR data processing and flight planning.

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Section: Figure 1 Conception Of Airborne Laser Scanningmentioning

confidence: 99%

Point Distribution as True Quality of LiDAR Point Cloud

Kodors¹

2017

BJMC

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“…These variable affecting the performance of registration leads to severe difficulty of its generalization. Even though a design of universal method applicable to all registration tasks is almost impossible, the majority of existing registration methods consist of following three typical steps; feature extraction, similarity measure and matching, and transformation (Brown, 1992;Habib et al, 2005). Thus, a successful registration depends on proper establishment of a strategy for individual steps.…”

Section: Introductionmentioning

confidence: 99%

Matching Aerial Images to 3d Building Models Based on Context-Based Geometric Hashing

Jung

Bang

Sohn

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission I, WG I/3KEY WORDS: Registration, 3D Building Models, Aerial Imagery, Geometric Hashing, Model to Image Matching ABSTRACT:In this paper, a new model-to-image framework to automatically align a single airborne image with existing 3D building models using geometric hashing is proposed. As a prerequisite process for various applications such as data fusion, object tracking, change detection and texture mapping, the proposed registration method is used for determining accurate exterior orientation parameters (EOPs) of a single image. This model-to-image matching process consists of three steps: 1) feature extraction, 2) similarity measure and matching, and 3) adjustment of EOPs of a single image. For feature extraction, we proposed two types of matching cues, edged corner points representing the saliency of building corner points with associated edges and contextual relations among the edged corner points within an individual roof. These matching features are extracted from both 3D building and a single airborne image. A set of matched corners are found with given proximity measure through geometric hashing and optimal matches are then finally determined by maximizing the matching cost encoding contextual similarity between matching candidates. Final matched corners are used for adjusting EOPs of the single airborne image by the least square method based on co-linearity equations. The result shows that acceptable accuracy of single image's EOP can be achievable by the proposed registration approach as an alternative to labourintensive manual registration process.

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“…The reliable linear features can be used as control entities to calculate the transformation parameters. Habib et al [7] used line features to register LiDAR point clouds and image data. For the image, the control lines were extracted manually; for the LiDAR point cloud, the line features were intersected by two near planes.…”

Section: Introductionmentioning

confidence: 99%

Surface-Based Registration of Airborne and Terrestrial Mobile LiDAR Point Clouds

Teo

Huang

2014

Remote Sensing

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Light Detection and Ranging (LiDAR) is an active sensor that can effectively acquire a large number of three-dimensional (3-D) points. LiDAR systems can be equipped on different platforms for different applications, but to integrate the data, point cloud registration is needed to improve geometric consistency. The registration of airborne and terrestrial mobile LiDAR is a challenging task because the point densities and scanning directions differ. We proposed a scheme for the registration of airborne and terrestrial mobile LiDAR using the least squares 3-D surface registration technique to minimize the surfaces between two datasets. To analyze the effect of point density in registration, the simulation data simulated different conditions and estimated the theoretical errors. The test data were the point clouds of the airborne LiDAR system (ALS) and the mobile LiDAR system (MLS), which were acquired by Optech ALTM 3070 and Lynx, respectively. The resulting simulation analysis indicated that the accuracy of registration improved as the density increased. For the test dataset, the registration error of mobile LiDAR between different trajectories improved from 40 cm to 4 cm, and the registration error between ALS and MLS improved from 84 cm to 4 cm. These results indicate that the proposed methods can obtain 5 cm accuracy between ALS and MLS.

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Photogrammetric and Lidar Data Registration Using Linear Features

Cited by 199 publications

References 6 publications

Point Distribution as True Quality of LiDAR Point Cloud

Point Distribution as True Quality of LiDAR Point Cloud

Matching Aerial Images to 3d Building Models Based on Context-Based Geometric Hashing

Surface-Based Registration of Airborne and Terrestrial Mobile LiDAR Point Clouds

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