Shiftable Leading Point Method for High Accuracy Registration of Airborne and Terrestrial LiDAR Data

Cheng, Liang; Tong, Lihua; Wu, Yang; Chen, Yanming; Li, Manchun

doi:10.3390/rs70201915

Cited by 16 publications

(10 citation statements)

References 41 publications

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“…When an ALS system is used to obtain surface 3D point clouds, it is difficult to set reference targets in the scanning scene, and this method is therefore more difficult to apply to analysis of ALS data registration results. Common point clouds, such as the ground points from ALS and MLS, can be selected from LiDAR point clouds and the offset distance between point clouds can then be calculated based on the common point [ 46 , 67 ]. Geographical scenes are unique and complex, and no geographical scenes have exactly the same geographical landscape; the geographical scene at the same location will also change with time.…”

Section: Error Analysis Methodsmentioning

confidence: 99%

Registration of Laser Scanning Point Clouds: A Review

Cheng

Chen

Liu

et al. 2018

Sensors

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205

109

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The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used for LiDAR point clouds registration. The coarse registration method is first used to achieve a good initial position, based on which registration is then refined utilizing the fine registration method. According to the coarse-to-fine framework, this paper reviews current registration methods and their methodologies, and identifies important differences between them. The lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods. The paper also describes the most commonly-used point cloud registration error analysis methods. Finally, avenues for future work on LiDAR data registration in terms of applications, data, and technology are discussed. In particular, there is a need to address registration of multi-angle and multi-scale data from various newly available types of LiDAR hardware, which will play an important role in diverse applications such as forest resource surveys, urban energy use, cultural heritage protection, and unmanned vehicles.

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Section: Error Analysis Methodsmentioning

confidence: 99%

Registration of Laser Scanning Point Clouds: A Review

Cheng

Chen

Liu

et al. 2018

Sensors

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205

109

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“…Building corners obtained from intersecting the building boundaries was used as primitive in the proposed method by Cheng et al (2013). Cheng, Tong, et al, (2015a) improved the geometric consistency of registration proposed in their previous work by compensating the geometric position of aerial corners in order to improve the consistency of conjugate corners set. Nevertheless, corner point extraction may be associated with large errors, and for buildings with eaves, there is a deviation for corners extracted from ALS and TLS.…”

Section: Introductionmentioning

confidence: 99%

Automatic hierarchical registration of aerial and terrestrial image-based point clouds

Baghani

Zoej

Mokhtarzade

2018

European Journal of Remote Sensing

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Point cloud registration has been a major research challenge in recent years. In this paper, a novel hierarchical method is proposed for registering Aerial and Terrestrial image-based Point Clouds (APC & TPC). This registration aims to yield a complete and dense coverage on both top and side faces in urban areas. The main challenges, however, arise from their heterogeneous views and very low overlap as well as the high discrepancies in scale and threedimensional rotations between the World Coordinate System (WCS) of APC and the Camera Coordinate System (CCS) of TPC. The proposed method begins by an innovative TPC-sufficient method to solve two rotations of TPC around x-and y-axis. After that, in horizontal registration phase, 2D façade lines were extracted from both datasets; and accordingly a novel polar parameterised mathematical model was extended for simultaneous robust matching and parameter estimation. Finally, vertical registration was done through calculating the dominant vertical shift between the points of the ground planes extracted from both datasets. The evaluation results in two different modes, which were conducted on two different urban datasets, showed the efficiency of the proposed method (RMSE error of 0.12 m/0.10 m in horizontal/vertical direction).

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“…However, because of the substantial difference between the two data, the combination of vertical and horizontal error cannot achieve better than 83 cm and 196 cm of mean and maximum error, respectively. Some other studies used airborne laser scanning (ALS) data to perform registration of terrestrial laser scanned (TLS) images [29][30][31][32][33]. However, because TLS has an entirely different error model compared to the MMS, the proposed methods cannot be applied to MMS calibration.…”

Section: Introductionmentioning

confidence: 99%

Towards High-Definition 3D Urban Mapping: Road Feature-Based Registration of Mobile Mapping Systems and Aerial Imagery

Javanmardi

et al. 2017

Remote Sensing

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Abstract:Various applications have utilized a mobile mapping system (MMS) as the main 3D urban remote sensing platform. However, the accuracy and precision of the three-dimensional data acquired by an MMS is highly dependent on the performance of the vehicle's self-localization, which is generally performed by high-end global navigation satellite system (GNSS)/inertial measurement unit (IMU) integration. However, GNSS/IMU positioning quality degrades significantly in dense urban areas with high-rise buildings, which block and reflect the satellite signals. Traditional landmark updating methods, which improve MMS accuracy by measuring ground control points (GCPs) and manually identifying those points in the data, are both labor-intensive and time-consuming. In this paper, we propose a novel and comprehensive framework for automatically georeferencing MMS data by capitalizing on road features extracted from high-resolution aerial surveillance data. The proposed framework has three key steps: (1) extracting road features from the MMS and aerial data; (2) obtaining Gaussian mixture models from the extracted aerial road features; and (3) performing registration of the MMS data to the aerial map using a dynamic sliding window and the normal distribution transform (NDT). The accuracy of the proposed framework is verified using field data, demonstrating that it is a reliable solution for high-precision urban mapping.

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Shiftable Leading Point Method for High Accuracy Registration of Airborne and Terrestrial LiDAR Data

Cited by 16 publications

References 41 publications

Registration of Laser Scanning Point Clouds: A Review

Registration of Laser Scanning Point Clouds: A Review

Automatic hierarchical registration of aerial and terrestrial image-based point clouds

Towards High-Definition 3D Urban Mapping: Road Feature-Based Registration of Mobile Mapping Systems and Aerial Imagery

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