Feature Conjugation for Intensity-Coded LIDAR Point Clouds

Han, Jen-Yu; Perng, Nei-Hao; Lin, Yan-Ting

doi:10.1061/(asce)su.1943-5428.0000106

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…LIDAR intensity is also used to detect common features in multiple sets of remote sensing data for registration. Methods using LIDAR intensity data have been developed for segmentation of multiple scans [ 10 , 11 , 12 , 13 , 14 ] and co-registration of scans and images [ 15 , 16 , 17 , 18 , 19 , 20 ].…”

Section: Applications Of Lidar Intensitymentioning

confidence: 99%

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration

Kashani

Olsen

Parrish

et al. 2015

Sensors

283

214

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In addition to precise 3D coordinates, most light detection and ranging (LIDAR) systems also record “intensity”, loosely defined as the strength of the backscattered echo for each measured point. To date, LIDAR intensity data have proven beneficial in a wide range of applications because they are related to surface parameters, such as reflectance. While numerous procedures have been introduced in the scientific literature, and even commercial software, to enhance the utility of intensity data through a variety of “normalization”, “correction”, or “calibration” techniques, the current situation is complicated by a lack of standardization, as well as confusing, inconsistent use of terminology. In this paper, we first provide an overview of basic principles of LIDAR intensity measurements and applications utilizing intensity information from terrestrial, airborne topographic, and airborne bathymetric LIDAR. Next, we review effective parameters on intensity measurements, basic theory, and current intensity processing methods. We define terminology adopted from the most commonly-used conventions based on a review of current literature. Finally, we identify topics in need of further research. Ultimately, the presented information helps lay the foundation for future standards and specifications for LIDAR radiometric calibration.

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Section: Applications Of Lidar Intensitymentioning

confidence: 99%

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration

Kashani

Olsen

Parrish

et al. 2015

Sensors

283

214

View full text Add to dashboard Cite

show abstract

“…The most known descriptors are Fast Point Feature Histograms (FPFH) [21], spin-images [22] and the Signature of Histograms of Orientations (SHOT) [23]. Another way to get keypoints is described in [24], where the points' intensity information is used to create 2D maps in which matching points are detected. Intensity information has also been used combined with a spatial descriptor to enhance the descriptiveness of the keypoints neighborhood [25].…”

Section: Related Workmentioning

confidence: 99%

Global Registration of 3D LiDAR Point Clouds Based on Scene Features: Application to Structured Environments

et al. 2017

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Acquiring 3D data with LiDAR systems involves scanning multiple scenes from different points of view. In actual systems, the ICP algorithm (Iterative Closest Point) is commonly used to register the acquired point clouds together to form a unique one. However, this method faces local minima issues and often needs a coarse initial alignment to converge to the optimum. This paper develops a new method for registration adapted to indoor environments and based on structure priors of such scenes. Our method works without odometric data or physical targets. The rotation and translation of the rigid transformation are computed separately, using, respectively, the Gaussian image of the point clouds and a correlation of histograms. To evaluate our algorithm on challenging registration cases, two datasets were acquired and are available for comparison with other methods online. The evaluation of our algorithm on four datasets against six existing methods shows that the proposed method is more robust against sampling and scene complexity. Moreover, the time performances enable a real-time implementation.Data Set: https://liris.cnrs.fr/3d-registration/ Data Set License: ODC Attribute License

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“…Therefore, a 3-D point cloud model of the measured object is generated in the local coordinate system. The laser scanner data, 15,[17][18][19] camera image, 20,21 or a combination of both 22 can be used in the registration of point clouds. ICP is the most widely used method in practice.…”

Section: Terrestrial Laser Scanningmentioning

confidence: 99%

Georeferencing of ground-based LIDAR data using continuously operating reference stations

2014

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Abstract. Terrestrial laser scanning has been used for various outdoor visualizations such as urban, construction, excavations, and land topography. Since laser scanning data have their own local coordinates in each station, a three-dimensional point cloud model of the object of interest is created in the local coordinate system by the combination of these measurements. For spatial queries and computations, the point cloud and other spatial data should be combined in a common coordinate system. In this study, a terrestrial laser scanner (TLS) and global navigation satellite system (GNSS) receiver were integrated for the registration of the laser scanner measurements into the geodetic coordinate system. Two georeferencing methods based on the continuously operating reference stations in the network Turkey (CORS-TR) were introduced. After the building was modelled by integrating the TLS and the GNSS receiver, the point cloud model that was created was registered to the international terrestrial reference frame. The registration was performed with 0.05 m root mean square error for the two georeferencing methods. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Feature Conjugation for Intensity-Coded LIDAR Point Clouds

Cited by 8 publications

References 23 publications

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration

Global Registration of 3D LiDAR Point Clouds Based on Scene Features: Application to Structured Environments

Georeferencing of ground-based LIDAR data using continuously operating reference stations

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