New Automated Point-Cloud Alignment for Ground-Based Light Detection and Ranging Data of Long Coastal Sections

Olsen, Michael J.; Johnstone, Elizabeth; Kuester, Falko; Driscoll, Neal W.; Ashford, Scott A.

doi:10.1061/(asce)su.1943-5428.0000030

Cited by 52 publications

(35 citation statements)

References 12 publications

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“…A "backsighting" technique was used during surveys to increase the efficiency of inter-scan alignment. The algorithms presented in Olsen et al (2011) were then applied to minimize the error throughout the survey section.…”

Section: Methodsmentioning

confidence: 99%

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Morphological Expressions of Coastal Cliff Erosion Processes in San Diego County

Johnstone

Raymond

Olsen

et al. 2016

Journal of Coastal Research

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Section: Methodsmentioning

confidence: 99%

“…Scans were georeferenced to minimize uncertainty using the methodologies outlined in Olsen et al (2009 and2011) for seacliff erosion studies. This approach has an estimated 3D accuracy of +/-7 cm with this equipment.…”

Section: Methodsmentioning

confidence: 99%

Morphological Expressions of Coastal Cliff Erosion Processes in San Diego County

Johnstone

Raymond

Olsen

et al. 2016

Journal of Coastal Research

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“…Olsen and Johnstone [9] proposed a registration method where the position of each scan spot is derived from GPS. This method is widely used in outdoor studies, but suffers from a lack of accuracy in the registration process because of the low accuracy of GPS.…”

Section: Geo Referencing Basedmentioning

confidence: 99%

Target-Free Automatic Registration of Point Clouds

Kim¹,

Cho²,

Chen³

2016

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-The aim of this paper is to introduce a novel method that automatically registers colored 3D point cloud sets without using targets or any other manual alignment processes. For fully automated point cloud registration without targets or landmarks, our approach utilizes feature detection algorithms used in computer vision. A digital camera and a laser scanner is utilized and the sensor data is merged based on a kinematic solution. The proposed approach is to detect and extract common features not directly from a 3D point cloud but from digital images corresponding to the point clouds. The initial alignment is achieved by matching common SURF features from corresponding digital images. Further alignment is obtained using plane segmentation and matching from the 3D point clouds. The test outcomes show promising results in terms of registration accuracy and processing time.

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“…The azimuthal values for scan positions are initially estimated using a digital compass and then converted into a rotation about the Z-axis (yaw). This angle is then improved using the least squares solution presented in Olsen et al [42], which calculates the optimal rotation of a scan about the Z-axis (centered at the scan origin) to best fit with nearby points in neighboring scans.…”

Section: Processingmentioning

confidence: 99%

“…For the 2013 and 2014 surveys, both the baseline and in-situ scans were geo-referenced following the methodology presented in [27,42] using the program PointReg. The methodology was developed for efficient surveying in dynamic environments (e.g., coastal areas, active landslides) where survey control can be difficult to establish and maintain.…”

Section: Processingmentioning

confidence: 99%

To Fill or Not to Fill: Sensitivity Analysis of the Influence of Resolution and Hole Filling on Point Cloud Surface Modeling and Individual Rockfall Event Detection

et al. 2015

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Monitoring unstable slopes with terrestrial laser scanning (TLS) has been proven effective. However, end users still struggle immensely with the efficient processing, analysis, and interpretation of the massive and complex TLS datasets. Two recent advances described in this paper now improve the ability to work with TLS data acquired on steep slopes. The first is the improved processing of TLS data to model complex topography and fill holes. This processing step results in a continuous topographic surface model that seamlessly characterizes the rock and soil surface. The second is an advance in the automated interpretation of the surface model in such a way that a magnitude and frequency relationship of rockfall events can be quantified, which can be used to assess maintenance strategies and forecast costs. The approach is applied to OPEN ACCESSRemote Sens. 2015, 7 12104 unstable highway slopes in the state of Alaska, U.S.A. to evaluate its effectiveness. Further, the influence of the selected model resolution and degree of hole filling on the derived slope metrics were analyzed. In general, model resolution plays a pivotal role in the ability to detect smaller rockfall events when developing magnitude-frequency relationships. The total volume estimates are also influenced by model resolution, but were comparatively less sensitive. In contrast, hole filling had a noticeable effect on magnitudefrequency relationships but to a lesser extent than modeling resolution. However, hole filling yielded a modest increase in overall volumetric quantity estimates. Optimal analysis results occur when appropriately balancing high modeling resolution with an appropriate level of hole filling.

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New Automated Point-Cloud Alignment for Ground-Based Light Detection and Ranging Data of Long Coastal Sections

Cited by 52 publications

References 12 publications

Morphological Expressions of Coastal Cliff Erosion Processes in San Diego County

Morphological Expressions of Coastal Cliff Erosion Processes in San Diego County

Target-Free Automatic Registration of Point Clouds

To Fill or Not to Fill: Sensitivity Analysis of the Influence of Resolution and Hole Filling on Point Cloud Surface Modeling and Individual Rockfall Event Detection

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