The past decade has seen an explosive increase in the number of peer reviewed papers reporting new scientific findings in geomorphology (including fans, channels, floodplains and landscape ...evolution), geologic mapping, tectonics and faulting, coastal processes, lava flows, hydrology (especially snow and runoff routing), glaciers and geo-archaeology. A common genesis of such findings is often newly available decimeter resolution 'bare Earth' geodetic images, derived from airborne laser swath mapping, a.k.a. airborne LiDAR, observations. In this paper we trace nearly a half century of advances in geodetic science made possible by space age technology, such as the invention of short-pulse-length high-pulse-rate lasers, solid state inertial measurement units, chip-based high speed electronics and the GPS satellite navigation system, that today make it possible to map hundreds of square kilometers of terrain in hours, even in areas covered with dense vegetation or shallow water. To illustrate the impact of the LiDAR observations we present examples of geodetic images that are not only stunning to the eye, but help researchers to develop quantitative models explaining how terrain evolved to its present form, and how it will likely change with time. Airborne LiDAR technology continues to develop quickly, promising ever more scientific discoveries in the years ahead.
A number of low-cost, small form factor, high resolution lidar sensors have recently been commercialized in an effort to fill the growing needs for lidar sensors on autonomous vehicles. These lidar ...sensors often report performance as range precision and angular accuracy, which are insufficient to characterize the overall quality of the point clouds returned by these sensors. Herein, a detailed geometric accuracy analysis of two representative autonomous sensors, the Ouster OSI-64 and the Livox Mid-40, is presented. The scanners were analyzed through a rigorous least squares adjustment of data from the two sensors using planar surface constraints. The analysis attempts to elucidate the overall point cloud accuracy and presence of systematic errors for the sensors over medium (< 40 m) ranges. The Livox Mid-40 sensor performance appears to be in conformance with the product specifications, with a ranging accuracy of approximately 2 cm. No significant systematic geometric errors were found in the acquired Mid-40 point clouds. The Ouster OSI-64 did not perform to the manufacturer specifications, with a ranging accuracy of 5.6 cm, which is nearly twice that stated by the manufacturer. Several of the individual lasers within the OSI-64’s bank of 64 lasers exhibited higher range noise than their counterparts, and examination of the residuals indicate a possible systematic error correlated with the horizontal encoder angle. This suggests that the Ouster laser may benefit from additional geometric calibration. Finally, both sensors suffered from an inability to accurately resolve edges and smaller features such as posts due to their large laser beam divergences.
AbstractThe registration and calibration of data captured with terrestrial laser scanner (TLS) instruments can be effectively achieved using signalized targets comprising components of both high and ...low reflectivity, so-called contrast targets. For projects requiring tens or even hundreds of such targets, the cost of manufacturer-constructed targets can be prohibitive. Moreover, the details of proprietary target center coordinate measurement algorithms are often not available to users. This paper reports on the design of a low-cost contrast target using readily available materials and an accompanying center measurement algorithm. Their compatibility with real terrestrial laser scanner data was extensively tested on six different instruments: two FARO Focus three-dimensional (3D) scanners, a Leica HDS6100, a Leica P40, a RIEGL VZ-400, and a Zoller+Fröhlich Imager 5010. Repeatability was examined as a function of range, incidence angle, sampling resolution, and target contrast. Performance in system self-calibration and from independent accuracy assessment is also reported. The results demonstrate compatibility for all five scanners. However, all data sets except the FARO Focus 3D require exclusion of observations made at high incidence angles in order to prevent range biases. Results also demonstrate that the spectral reflectivity of the target components is critical to ensure high contrast between target components, and therefore high-quality target center coordinate measurements.
CALIBRATION AND STABILITY ANALYSIS OF THE VLP-16 LASER SCANNER Glennie, C. L.; Kusari, A.; Facchin, A.
International archives of the photogrammetry, remote sensing and spatial information sciences.,
03/2016, Volume:
XL-3/W4
Journal Article, Conference Proceeding
Peer reviewed
Open access
We report on a calibration and stability analysis of the Velodyne VLP-16 LiDAR scanner. The sensor is evaluated for long-term stability, geometric calibration and the effect of temperature ...variations. To generalize the results, three separate VLP-16 sensors were examined. The results and conclusions from the analysis of each of the individual sensors was similar. We found that the VLP-16 showed a consistent level of performance, in terms of range bias and noise level over the tested temperature range from 0-40 °C. A geometric calibration was able to marginally improve the accuracy of the VLP-16 point cloud (by approximately 20%) for a single collection, however the temporal stability of the geometric calibration negated this accuracy improvement. Overall, it was found that there is some long-term walk in the ranging observations from individual lasers within the VLP-16, which likely causes the instability in the determination of geometric calibration parameters. However, despite this range walk, the point cloud delivered from the VLP-16 sensors tested showed an accuracy level within the manufacturer specifications of 3 cm RMSE, with an overall estimated RMSE of range residuals between 22 mm and 27 mm.
The so-called seam line discontinuity is a phenomenon that can be observed in point clouds captured with some panoramic terrestrial laser scanners. It is an angular discontinuity that is most ...apparent where the lower limit of the instrument’s angular field-of-view intersects the ground. It appears as step discontinuities at the start (0° horizontal direction) and end (180°) of scanning. To the authors’ best knowledge, its cause and its impact, if any, on point cloud accuracy have not yet been reported. This paper presents the results of a rigorous investigation into several hypothesized causes of this phenomenon: differences between the lower and upper elevation angle scanning limits; the presence of a vertical circle index error; and changes in levelling during scanning. New models for the angular observations have been developed and simulations were performed to independently study the impact of each hypothesized cause and to guide the analyses of real datasets. In order to scrutinize each of the hypothesized causes, experiments were conducted with seven real datasets captured with six different instruments: one hybrid-architecture scanner and five panoramic scanners, one of which was also operated as a hybrid instrument. This study concludes that the difference between the elevation angle scanning limits is the source of the seam line discontinuity phenomenon. Accuracy assessment experiments over real data captured in an indoor test facility demonstrate that the seam line discontinuity has no metric impact on the point clouds.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
CALIBRATION AND STABILITY ANALYSIS OF THE VLP-16 LASER SCANNER Glennie, C. L.; Kusari, A.; Facchin, A.
International archives of the photogrammetry, remote sensing and spatial information sciences.,
03/2016, Volume:
XL-3/W4
Journal Article, Conference Proceeding
Peer reviewed
Open access
We report on a calibration and stability analysis of the Velodyne VLP-16 LiDAR scanner. The sensor is evaluated for long-term stability, geometric calibration and the effect of temperature ...variations. To generalize the results, three separate VLP-16 sensors were examined. The results and conclusions from the analysis of each of the individual sensors was similar. We found that the VLP-16 showed a consistent level of performance, in terms of range bias and noise level over the tested temperature range from 0–40 °C. A geometric calibration was able to marginally improve the accuracy of the VLP-16 point cloud (by approximately 20%) for a single collection, however the temporal stability of the geometric calibration negated this accuracy improvement. Overall, it was found that there is some long-term walk in the ranging observations from individual lasers within the VLP-16, which likely causes the instability in the determination of geometric calibration parameters. However, despite this range walk, the point cloud delivered from the VLP-16 sensors tested showed an accuracy level within the manufacturer specifications of 3 cm RMSE, with an overall estimated RMSE of range residuals between 22 mm and 27 mm.
Remote sensing via LiDAR (Light Detection And Ranging) has proven extremely useful in both Earth science and hazard related studies. Surveys taken before and after an earthquake for example, can ...provide decimeter-level, 3D near-field estimates of land deformation that offer better spatial coverage of the near field rupture zone than other geodetic methods (e.g., InSAR, GNSS, or alignment array). In this study, we compare and contrast estimates of deformation obtained from different pre and post-event airborne laser scanning (ALS) data sets of the 2014 South Napa Earthquake using two change detection algorithms, Iterative Control Point (ICP) and Particle Image Velocimetry (PIV). The ICP algorithm is a closest point based registration algorithm that can iteratively acquire three dimensional deformations from airborne LiDAR data sets. By employing a newly proposed partition scheme, “moving window,” to handle the large spatial scale point cloud over the earthquake rupture area, the ICP process applies a rigid registration of data sets within an overlapped window to enhance the change detection results of the local, spatially varying surface deformation near-fault. The other algorithm, PIV, is a well-established, two dimensional image co-registration and correlation technique developed in fluid mechanics research and later applied to geotechnical studies. Adapted here for an earthquake with little vertical movement, the 3D point cloud is interpolated into a 2D DTM image and horizontal deformation is determined by assessing the cross-correlation of interrogation areas within the images to find the most likely deformation between two areas. Both the PIV process and the ICP algorithm are further benefited by a presented, novel use of urban geodetic markers. Analogous to the persistent scatterer technique employed with differential radar observations, this new LiDAR application exploits a classified point cloud dataset to assist the change detection algorithms. Ground deformation results and statistics from these techniques are presented and discussed here with supplementary analyses of the differences between techniques and the effects of temporal spacing between LiDAR datasets. Results show that both change detection methods provide consistent near field deformation comparable to field observed offsets. The deformation can vary in quality but estimated standard deviations are always below thirty one centimeters. This variation in quality differentiates the methods and proves that factors such as geodetic markers and temporal spacing play major roles in the outcomes of ALS change detection surveys.
Capturing and quantifying the world in three dimensions (x,y,z) using light detection and ranging (lidar) technology drives fundamental advances in the Earth and Ecological Sciences (EES). However, ...additional lidar dimensions offer the possibility to transcend basic 3-D mapping capabilities, including i) the physical time (t) dimension from repeat lidar acquisition and ii) laser return intensity (LRIλ) data dimension based on the brightness of single- or multi-wavelength (λ) laser returns. The additional dimensions thus add to the x,y, and z dimensions to constitute the five dimensions of lidar (x,y,z, t, LRIλ1… λn). This broader spectrum of lidar dimensionality has already revealed new insights across multiple EES topics, and will enable a wide range of new research and applications. Here, we review recent advances based on repeat lidar collections and analysis of LRI data to highlight novel applications of lidar remote sensing beyond 3-D. Our review outlines the potential and current challenges of time and LRI information from lidar sensors to expand the scope of research applications and insights across the full range of EES applications.
•X, y, z, time, and laser return intensity constitute the 5-dimensions of LiDAR.•We review recent advances to highlight novel applications of LiDAR beyond 3D.•Beyond 3D LiDAR has and will enable a wide range of new research and applications.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Remote sensing via LiDAR (Light Detection And Ranging) has proven extremely useful in both Earth science and hazard related studies. Surveys taken before and after an earthquake for example, can ...provide decimeter-level, 3D near-field estimates of land deformation that offer better spatial coverage of the near field rupture zone than other geodetic methods (e.g., InSAR, GNSS, or alignment array). In this study, we compare and contrast estimates of deformation obtained from different pre and post-event airborne laser scanning (ALS) data sets of the 2014 South Napa Earthquake using two change detection algorithms, Iterative Control Point (ICP) and Particle Image Velocimetry (PIV). The ICP algorithm is a closest point based registration algorithm that can iteratively acquire three dimensional deformations from airborne LiDAR data sets. By employing a newly proposed partition scheme, “moving window,” to handle the large spatial scale point cloud over the earthquake rupture area, the ICP process applies a rigid registration of data sets within an overlapped window to enhance the change detection results of the local, spatially varying surface deformation near-fault. The other algorithm, PIV, is a well-established, two dimensional image co-registration and correlation technique developed in fluid mechanics research and later applied to geotechnical studies. Adapted here for an earthquake with little vertical movement, the 3D point cloud is interpolated into a 2D DTM image and horizontal deformation is determined by assessing the cross-correlation of interrogation areas within the images to find the most likely deformation between two areas. Both the PIV process and the ICP algorithm are further benefited by a presented, novel use of urban geodetic markers. Analogous to the persistent scatterer technique employed with differential radar observations, this new LiDAR application exploits a classified point cloud dataset to assist the change detection algorithms. Ground deformation results and statistics from these techniques are presented and discussed here with supplementary analyses of the differences between techniques and the effects of temporal spacing between LiDAR datasets. Results show that both change detection methods provide consistent near field deformation comparable to field observed offsets. The deformation can vary in quality but estimated standard deviations are always below thirty one centimeters. This variation in quality differentiates the methods and proves that factors such as geodetic markers and temporal spacing play major roles in the outcomes of ALS change detection surveys.
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