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

Teo, Tee-Ann; Huang, Shih Han

doi:10.3390/rs61212686

Cited by 18 publications

(18 citation statements)

References 21 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The non-georeferenced data sets can be co-registered with this large, georeferenced 'block', thus bringing the entire multi-source point cloud to a single, known coordinate system [48]. In addition, it is possible to utilize ALS data for improving the registration of MLS systems, operating in varying GNSS visibility [49].…”

Section: Co-registrationmentioning

confidence: 99%

Nationwide Point Cloud—The Future Topographic Core Data

Virtanen

Kukko

Kaartinen

et al. 2017

IJGI

View full text Add to dashboard Cite

Topographic databases maintained by national mapping agencies are currently the most common nationwide data sets in geo-information. The application of laser scanning as source data for surveying is increasing. Along with this development, several analysis methods that utilize dense point clouds have been introduced. We present the concept of producing a dense nationwide point cloud, produced from multiple sensors and containing multispectral information, as the national core data for geo-information. Geo-information products, such as digital terrain and elevation models and 3D building models, are produced automatically from these data. We outline the data acquisition, processing, and application of the point cloud. As a national data set, a dense multispectral point cloud could produce significant cost savings via improved automation in mapping and a reduction of overlapping surveying efforts.

show abstract

Section: Co-registrationmentioning

confidence: 99%

Nationwide Point Cloud—The Future Topographic Core Data

Virtanen

Kukko

Kaartinen

et al. 2017

IJGI

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…The workflow of the multi-feature registration involves three steps comprising the feature extraction, feature matching, and transformation estimation. Although considerable researches on extracting point, line [51], and plane [49,52] features from point clouds have been reported, fully automated feature extraction is still a relevant research topic. We leveraged a rapid plane extraction [53] coupled with a line extractor [51] to acquire accurate and evenly-distributed features and then performed the multiple feature matching approach called RSTG [31] to confirm the conjugate features and to derive approximations for the nonlinear transformation estimation model.…”

Section: Introductionmentioning

confidence: 99%

“…Teo and Huang [49] and Cheng et al [50] proposed a surface-based and a point-based registration, respectively, for airborne and ground-based datasets. However, the inadequate overlapping area of the point clouds of the ALS and MLS or TLS as the scenarios demonstrated in Figure 1a,b raises challenges for acquiring sufficient conjugate features and thus is apt to result in unqualified transformation estimation.…”

Section: Introductionmentioning

confidence: 99%

Multi-Feature Registration of Point Clouds

Chuang

Jaw

2017

Remote Sensing

View full text Add to dashboard Cite

Light detection and ranging (LiDAR) has become a mainstream technique for rapid acquisition of 3-D geometry. Current LiDAR platforms can be mainly categorized into spaceborne LiDAR system (SLS), airborne LiDAR system (ALS), mobile LiDAR system (MLS), and terrestrial LiDAR system (TLS). Point cloud registration between different scans of the same platform or different platforms is essential for establishing a complete scene description and improving geometric consistency. The discrepancies in data characteristics should be manipulated properly for precise transformation estimation. This paper proposes a multi-feature registration scheme suitable for utilizing point, line, and plane features extracted from raw point clouds to realize the registrations of scans acquired within the same LIDAR system or across the different platforms. By exploiting the full geometric strength of the features, different features are used exclusively or combined with others. The uncertainty of feature observations is also considered within the proposed method, in which the registration of multiple scans can be simultaneously achieved. The simulated test with an ideal geometry and data simplification was performed to assess the contribution of different features towards point cloud registration in a very essential fashion. On the other hand, three real cases of registration between LIDAR scans from single platform and between those acquired by different platforms were demonstrated to validate the effectiveness of the proposed method. In light of the experimental results, it was found that the proposed model with simultaneous and weighted adjustment rendered satisfactory registration results and showed that not only features inherited in the scene can be more exploited to increase the robustness and reliability for transformation estimation, but also the weak geometry of poorly overlapping scans can be better treated than utilizing only one single type of feature. The registration errors of multiple scans in all tests were all less than point interval or positional error, whichever dominating, of the LiDAR data.

show abstract

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

Cited by 18 publications

References 21 publications

Nationwide Point Cloud—The Future Topographic Core Data

Nationwide Point Cloud—The Future Topographic Core Data

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

Multi-Feature Registration of Point Clouds

Contact Info

Product

Resources

About