3D LIDAR–camera intrinsic and extrinsic calibration: Identifiability and analytical least-squares-based initialization

Mirzaei, Faraz M.; Kottas, Dimitrios G.; Roumeliotis, Stergios I.

doi:10.1177/0278364911435689

Cited by 145 publications

(104 citation statements)

References 18 publications

(23 reference statements)

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“…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). In summary, these prior studies have demonstrated that for both the HDL-64E and HDL-32E the factory calibration of the instruments was not optimized, that the instruments exhibited temporal instability and also required a significant warm-up period to reach steady-state ( (Glennie et al, 2013;Glennie andLichti, 2010, 2011).…”

Section: Introductionmentioning

confidence: 91%

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: 91%

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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“…Furthermore, the use of the light source prevents the algorithm from operating outdoors such as may be required in-situ. Mirzaei et al [19] simultaneously calibrate the internal and external sensor parameters, however, their algorithm is specific to the Velodyne. The convenience of circular calibration targets have been previously exploited by Guan et al [11] to calibrate a network of camcorders and ToF cameras.…”

Section: External Parametersmentioning

confidence: 99%

Automatic Calibration of a Range Sensor and Camera System

Alismail

Baker

Browning

2012

2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization &Amp; Transmission

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We propose an automated method to recover the full calibration parameters between a 3D range sensor and a monocular camera system. Our method is not only accurate and fully automated, but also relies on a simple calibration target consisting of a single circle. This allows the algorithm to be suitable for applications requiring in-situ calibration. We demonstrate the effectiveness of the algorithm on a cameralidar system and show results on 3D mapping tasks.

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“…Figure 1: Top view of a point cloud representing a wall area. Mirzaei et al (2012) showed how to calibrate a multi-beam system with a rigidly connected camera (intrinsic and extrinsic) by observing a planar calibration board with fiducial markers. The camera supplies the position and orientation of the calibration board.…”

Section: Related Workmentioning

confidence: 99%

Calibration of a multi-beam Laser System by using a TLS-generated Reference

Gordon

Meidow

2013

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Rotating multi-beam LIDARs mounted on moving platforms have become very successful for many applications such as autonomous navigation, obstacle avoidance or mobile mapping. To obtain accurate point coordinates, a precise calibration of such a LIDAR system is required. For the determination of the corresponding parameters we propose a calibration scheme which exploits the information of 3D reference point clouds captured by a terrestrial laser scanning (TLS) device. It is assumed that the accuracy of this point clouds is considerably higher than that from the multi-beam LIDAR and that the data represent faces of man-made objects at different distances. After extracting planes in the reference data sets, the point-plane-incidences of the measured points and the reference planes are used to formulate the implicit constraints. We inspect the Velodyne HDL-64E S2 system as the best-known representative for this kind of sensor system. The usability and feasibility of the calibration procedure is demonstrated with real data sets representing building faces (walls, roof planes and ground). Beside the improvement of the point accuracy by considering the calibration results, we test the significance of the parameters related to the sensor model and consider the uncertainty of measurements w.r.t. the measured distances. The Velodyne returns two kinds of measurements-distances and encoder angles. To account for this, we perform a variance component estimation to obtain realistic standard deviations for the observations.

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3D LIDAR–camera intrinsic and extrinsic calibration: Identifiability and analytical least-squares-based initialization

Abstract: In this paper we address the problem of estimating the intrinsic parameters of a 3D LIDAR

Cited by 145 publications

References 18 publications

Calibration and Stability Analysis of the VLP-16 Laser Scanner

Calibration and Stability Analysis of the VLP-16 Laser Scanner

Automatic Calibration of a Range Sensor and Camera System

Calibration of a multi-beam Laser System by using a TLS-generated Reference

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