Error propagation and uncertainty analysis between 3D laser scanner and camera

García-Moreno, Angel-Iván; Hernandez-Garcia, Denis-Eduardo; González-Barbosa, José-Joel; Ramírez-Pedraza, Alfonso; Hurtado-Ramos, Juan B.; Ornelas-Rodríguez, Francisco-Javier

doi:10.1016/j.robot.2014.02.004

Cited by 11 publications

(7 citation statements)

References 17 publications

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“…Sensitivity tests for the calibration method presented in [18] were performed. It was concluded that the parameters involved in the distance between the camera and the calibration pattern (depth) are the most likely to introduce error in the final values of camera calibration, as also concluded in uncertainty analysis in [6] Now that the system behaviour is known, we can pay more attention to characterize the uncertainty in these parameters. LHS method tends to calculate more stable results, i.e.…”

Section: Discussionmentioning

confidence: 95%

“…Furthermore, also the parameter with greater total sensitivity is ߚ. A similar behavior but analyzed from the error propagation perspective in LiDAR-camera calibration is presented in [6]. We can define that the parameters involved directly with the distance of the calibration pattern and image distortions tend to be the most relevant in the error propagation in our calibration system.…”

mentioning

confidence: 91%

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Sensitivity Analysis in a Lidar-Camera Calibration

García-Moreno¹,

González-Barbosa²,

Hurtado-Ramos³

et al. 2015

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Section: Discussionmentioning

confidence: 95%

mentioning

confidence: 91%

Sensitivity Analysis in a Lidar-Camera Calibration

García-Moreno¹,

González-Barbosa²,

Hurtado-Ramos³

et al. 2015

Computer Science &Amp; Information Technology ( CS &Amp; IT )

Self Cite

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“…There are also some methods that automatically extract feature points, such as vertices of a polygonal planar checkerboard, from the LiDAR data. Nevertheless, these approaches require either manual operation for feature points selection from the image [10] or customized checkerboard for feature points generation in the point cloud [11]. Geiger et al proposed an automatic method for extrinsic calibration with one shot of multiple checkerboards in [23].…”

Section: Multiple Geometry Elementsmentioning

confidence: 99%

“…For target-based calibration, the conventional method involves finding the vertices of a polygonal board, which can be a chessboard or a triangular board, both in the point cloud obtained by the LiDAR and the image captured by the camera either manually or automatically [9,10,11]. The vertices are estimated by constructing the convex hull of the extracted board's point cloud.…”

Section: Introductionmentioning

confidence: 99%

Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard

2017

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This paper presents a novel method for fully automatic and convenient extrinsic calibration of a 3D LiDAR and a panoramic camera with a normally printed chessboard. The proposed method is based on the 3D corner estimation of the chessboard from the sparse point cloud generated by one frame scan of the LiDAR. To estimate the corners, we formulate a full-scale model of the chessboard and fit it to the segmented 3D points of the chessboard. The model is fitted by optimizing the cost function under constraints of correlation between the reflectance intensity of laser and the color of the chessboard's patterns. Powell's method is introduced for resolving the discontinuity problem in optimization. The corners of the fitted model are considered as the 3D corners of the chessboard. Once the corners of the chessboard in the 3D point cloud are estimated, the extrinsic calibration of the two sensors is converted to a 3D-2D matching problem. The corresponding 3D-2D points are used to calculate the absolute pose of the two sensors with Unified Perspective-n-Point (UPnP). Further, the calculated parameters are regarded as initial values and are refined using the Levenberg-Marquardt method. The performance of the proposed corner detection method from the 3D point cloud is evaluated using simulations. The results of experiments, conducted on a Velodyne HDL-32e LiDAR and a Ladybug3 camera under the proposed re-projection error metric, qualitatively and quantitatively demonstrate the accuracy and stability of the final extrinsic calibration parameters.

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“…In some particular applications, the combination of multiple vision systems can synthesize the advantages of the member measurement devices and achieve comprehensive measurement results [8,[12][13][14][15][16][17][18]. However, more errors and measurement uncertainty are introduced to such combined systems that affect the overall 3D reconstruction accuracy [19]. Hence, it is important to evaluate the measurement error and uncertainty of the combined visual system in order to perform error compensation and improve overall measurement accuracy.…”

Section: Introductionmentioning

confidence: 99%

Calibration and uncertainty analysis of a combined tracking-based vision measurement system using Monte Carlo simulation

Jiang¹,

Cui²,

Cheng³

et al. 2021

Meas. Sci. Technol.

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A global stereovision system combined with a local vision sensor is an effective approach to large-scale object measurement. However, obtaining the error distribution of such an approach remains a key research challenge in vision metrological applications. This paper investigates the calibration and the reconstruction uncertainty estimation method of the combined vision system. The measurement principle and the calibration method of the transformation matrix between the tracking-based measurement coordinate systems are presented. Furthermore, Monte Carlo simulation is utilized to determine the reconstruction uncertainty based on the theoretical measurement model and the experiment-based input uncertainty. The overall measurement uncertainty of the combined system is found to be 34.5% higher than that of the global vision system, which is more sensitive to the input pixel uncertainty than the local vision system. However, the combined vision system can achieve comparable measurement results within its larger working volume. This work contributes to a better understanding of the measurement uncertainty in combined tracking-based vision systems, as well as providing a few useful practice guidelines for using such a visual system.

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Error propagation and uncertainty analysis between 3D laser scanner and camera

Cited by 11 publications

References 17 publications

Sensitivity Analysis in a Lidar-Camera Calibration

Sensitivity Analysis in a Lidar-Camera Calibration

Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard

Calibration and uncertainty analysis of a combined tracking-based vision measurement system using Monte Carlo simulation

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