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

Wang, Weimin; Satoh, Ken; Kawaguchi, Nobuo

doi:10.3390/rs9080851

Cited by 81 publications

(28 citation statements)

References 31 publications

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“…In the multisensor software framework to each incoming data frame, a timestamp—obtained through NTP—was assigned, based on which an accurate time synchronization could be achieved. For the calibration of cameras, a flexible chessboard based technique has been used [ 30 ] while the estimation of camera-LIDAR extrinsics was performed based on the method shown in [ 31 ].…”

Section: Measurement Campaign Realizationmentioning

confidence: 99%

Motorway Measurement Campaign to Support R&D Activities in the Field of Automated Driving Technologies

Tihanyi

Tettamanti

Csonthó

et al. 2021

Sensors

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A spectacular measurement campaign was carried out on a real-world motorway stretch of Hungary with the participation of international industrial and academic partners. The measurement resulted in vehicle based and infrastructure based sensor data that will be extremely useful for future automotive R&D activities due to the available ground truth for static and dynamic content. The aim of the measurement campaign was twofold. On the one hand, road geometry was mapped with high precision in order to build Ultra High Definition (UHD) map of the test road. On the other hand, the vehicles—equipped with differential Global Navigation Satellite Systems (GNSS) for ground truth localization—carried out special test scenarios while collecting detailed data using different sensors. All of the test runs were recorded by both vehicles and infrastructure. The paper also showcases application examples to demonstrate the viability of the collected data having access to the ground truth labeling. This data set may support a large variety of solutions, for the test and validation of different kinds of approaches and techniques. As a complementary task, the available 5G network was monitored and tested under different radio conditions to investigate the latency results for different measurement scenarios. A part of the measured data has been shared openly, such that interested automotive and academic parties may use it for their own purposes.

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Section: Measurement Campaign Realizationmentioning

confidence: 99%

Motorway Measurement Campaign to Support R&D Activities in the Field of Automated Driving Technologies

Tihanyi

Tettamanti

Csonthó

et al. 2021

Sensors

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“…The method was applied in the KITTI dataset [15] to calibrate the extrinsic parameters between the cameras and the LiDAR sensor. Unlike the approaches above, Wang et al [16] utilizes the reflectance intensity to estimate the corners of the chessboard from the 3D laser point cloud. If the corners of the 3D laser point cloud are identified, the extrinsic calibration is converted to a 3D-2D matching problem.…”

Section: Lidarcamera Calibration Under Arbitrary Configurations: Obsementioning

confidence: 99%

LiDAR-Camera Calibration Under Arbitrary Configurations: Observability and Methods

Wang

Ding

et al. 2020

IEEE Trans. Instrum. Meas.

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LiDAR-camera calibration is a precondition for many heterogeneous systems that fuse data from LiDAR and camera. However, the constraint from common field of view and the requirement for strict time synchronization make the calibration a challenging problem. In this paper, we propose a novel LiDAR-camera calibration method aiming to eliminate these two constraints. Specifically, we capture a scan of 3D LiDAR when both the environment and the sensors are stationary, then move the camera to reconstruct the 3D environment using the sequentially obtained images. Finally, we align 3D visual points to the laser scan based on tightly couple graph optimization method to calculate the extrinsic parameters between LiDAR and camera. Under this design, the configuration of these two sensors are free from the common field of view constraint owing to the extended view from the moving camera. And we also eliminate the requirement for strict time synchronization as we only use the single scan of laser data when the sensors are stationary. We theoretically derive the conditions of minimal observability for our method and prove that the accuracy of calibration is improved by collecting more observations from multiple scattered calibration targets. We validate our method on both simulation platform and real-world datasets. Experiments show that our method achieves higher accuracy than other comparable methods, which is in accordance with our theoretical analysis. In addition, the proposed method is beneficial to not only plane measurement error based chessboard, but also other point measurement error based calibration targets, such as boxes and polygonal boards.

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“…If no further information about the environment is given, this part is better done manually. One could use retro-reflective material to cover the box and use the reflectivity information of the LiDAR sensors to aid the automatic detection [ 18 , 19 ] of the box. However, our method does not need special materials.…”

Section: Estimation Of Box Corners In the Lidar Point Cloudmentioning

confidence: 99%

Accurate Calibration of Multi-LiDAR-Multi-Camera Systems

Pusztai

Eichhardt

Hajder

2018

Sensors

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As autonomous driving attracts more and more attention these days, the algorithms and sensors used for machine perception become popular in research, as well. This paper investigates the extrinsic calibration of two frequently-applied sensors: the camera and Light Detection and Ranging (LiDAR). The calibration can be done with the help of ordinary boxes. It contains an iterative refinement step, which is proven to converge to the box in the LiDAR point cloud, and can be used for system calibration containing multiple LiDARs and cameras. For that purpose, a bundle adjustment-like minimization is also presented. The accuracy of the method is evaluated on both synthetic and real-world data, outperforming the state-of-the-art techniques. The method is general in the sense that it is both LiDAR and camera-type independent, and only the intrinsic camera parameters have to be known. Finally, a method for determining the 2D bounding box of the car chassis from LiDAR point clouds is also presented in order to determine the car body border with respect to the calibrated sensors.

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

Cited by 81 publications

References 31 publications

Motorway Measurement Campaign to Support R&D Activities in the Field of Automated Driving Technologies

Motorway Measurement Campaign to Support R&D Activities in the Field of Automated Driving Technologies

LiDAR-Camera Calibration Under Arbitrary Configurations: Observability and Methods

Accurate Calibration of Multi-LiDAR-Multi-Camera Systems

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