Automatic In Situ Calibration of a Spinning Beam LiDAR System in Static and Kinematic Modes

Chan, Ting On; Lichti, Derek D.

doi:10.3390/rs70810480

Cited by 20 publications

(13 citation statements)

References 24 publications

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“…The predecessors of the VLP-16 were the HDL-32E and HDL-64E, both of which have been extensively studied in the literature to examine both their performance and calibration. For example, (Muhammad and Lacroix, 2010), calibrated an HDL-64E using manually extracted wall surfaces while (Chen and Chien, 2012) used an automatic RANSAC-based plane detection algorithm to extract vertical walls for evaluation of the HDL-64E, (Atanacio-Jiménez et al, 2011) used larged cuboid control targets to calibrate their HDL-64E, and (Chan and Lichti, 2015) utilized cylindrical targets such as lampposts to calibrate a HDL-32E sensor. The calibration and accuracy of the previous generation Velodyne laser scanners has also been reported when they are fused with other sensors in a mobile mapping system, such as the fusion of and HDL-64E and Ladybug camera reported in (Gong et al, 2013) and (Mirzaei et al, 2012), and the combination of an HDL-32E and frame camera in (Park et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Calibration and Stability Analysis of the VLP-16 Laser Scanner

Glennie

Kusari

Facchin³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT: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.

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Section: Introductionmentioning

confidence: 99%

Calibration and Stability Analysis of the VLP-16 Laser Scanner

Glennie

Kusari

Facchin³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…For the sensor integration of the MMS, the mathematical models with accurate parameters to transform each observation data into another sensor system or an absolute coordinates system must be defined, and Figure 5 describes the conceptual model of the MMS [33,43,53,54,55]. …”

Section: Methodsmentioning

confidence: 99%

“…Alternatively, the line, plane, and cylindrical features, which can be defined by mathematical equations, have been widely applied for the calibration of laser scanners [33,34,35]. For example, the plane features can be precisely extracted from a sparse point cloud using a RANdom SAmple Consensus (RANSAC) algorithm even if there exist noisy points in the point cloud [36].…”

Section: Introductionmentioning

confidence: 99%

Utilization of a Terrestrial Laser Scanner for the Calibration of Mobile Mapping Systems

Hong

Park

Lee

et al. 2017

Sensors

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This paper proposes a practical calibration solution for estimating the boresight and lever-arm parameters of the sensors mounted on a Mobile Mapping System (MMS). On our MMS devised for conducting the calibration experiment, three network video cameras, one mobile laser scanner, and one Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) were mounted. The geometric relationships between three sensors were solved by the proposed calibration, considering the GNSS/INS as one unit sensor. Our solution basically uses the point cloud generated by a 3-dimensional (3D) terrestrial laser scanner rather than using conventionally obtained 3D ground control features. With the terrestrial laser scanner, accurate and precise reference data could be produced and the plane features corresponding with the sparse mobile laser scanning data could be determined with high precision. Furthermore, corresponding point features could be extracted from the dense terrestrial laser scanning data and the images captured by the video cameras. The parameters of the boresight and the lever-arm were calculated based on the least squares approach and the precision of the boresight and lever-arm could be achieved by 0.1 degrees and 10 mm, respectively.

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“…The second category includes methods based on least-square (LS) estimation of calibration parameters through conditioning points lying on specific features, e.g. planar features (Glennie et al, 2016), linear features (Le Scouarnec et al, 2014), catenary features (Chan et al, 2013), and pole-like features (Chan et al, 2015). Due to depending on INS/GPS observations, all the studies need to consider the level of uncertainty of these observations in the self-calibration adjustment and to re-perform the calibration to measure bore-sight and lever-arm uncompensated errors during the flight when the INS/GPS observations become more accurate.…”

Section: Calibrationmentioning

confidence: 99%

UAV-LiCAM SYSTEM DEVELOPMENT: CALIBRATION AND GEO-REFERENCING

Cortés¹,

Shahbazi²,

Ménard³

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> In the last decade, applications of unmanned aerial vehicles (UAVs), as remote-sensing platforms, have extensively been investigated for fine-scale mapping, modeling and monitoring of the environment. In few recent years, integration of 3D laser scanners and cameras onboard UAVs has also received considerable attention as these two sensors provide complementary spatial/spectral information of the environment. Since lidar performs range and bearing measurements in its body-frame, precise GNSS/INS data are required to directly geo-reference the lidar measurements in an object-fixed coordinate system. However, such data comes at the price of tactical-grade inertial navigation sensors enabled with dual-frequency RTK-GNSS receivers, which also necessitates having access to a base station and proper post-processing software. Therefore, such UAV systems equipped with lidar and camera (UAV-LiCam Systems) are too expensive to be accessible to a wide range of users. Hence, new solutions must be developed to eliminate the need for costly navigation sensors. In this paper, a two-fold solution is proposed based on an in-house developed, low-cost system: 1) a multi-sensor self-calibration approach for calibrating the Li-Cam system based on planar and cylindrical multi-directional features; 2) an integrated sensor orientation method for georeferencing based on unscented particle filtering which compensates for time-variant IMU errors and eliminates the need for GNSS measurements.</p>

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Automatic In Situ Calibration of a Spinning Beam LiDAR System in Static and Kinematic Modes

Cited by 20 publications

References 24 publications

Calibration and Stability Analysis of the VLP-16 Laser Scanner

Calibration and Stability Analysis of the VLP-16 Laser Scanner

Utilization of a Terrestrial Laser Scanner for the Calibration of Mobile Mapping Systems

UAV-LiCAM SYSTEM DEVELOPMENT: CALIBRATION AND GEO-REFERENCING

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