Line Feature Based Extrinsic Calibration of LiDAR and Camera

“…The limitation of these methods is that good parameters of the off-line calibration can not always accurately calibrate LiDAR and camera after environmental changes or vibrations like some bumps and jolts in real-time applications. Traditional online methods [11]- [13] are proposed to overcome these deficiencies. These methods can gradually converge to accurate parameters by handling continuously input images and point clouds.…”

Section: Related Workmentioning

confidence: 99%

CalibRCNN: Calibrating Camera and LiDAR by Recurrent Convolutional Neural Network and Geometric Constraints

Shi

Zhu

Zhang

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this paper, we present Calibration Recurrent Convolutional Neural Network (CalibRCNN) to infer a 6 degrees of freedom (DOF) rigid body transformation between 3D LiDAR and 2D camera. Different from the existing methods, our 3D-2D CalibRCNN not only uses the LSTM network to extract the temporal features between 3D point clouds and RGB images of consecutive frames, but also uses the geometric loss and photometric loss obtained by the interframe constraint to refine the calibration accuracy of the predicted transformation parameters. The CalibRCNN aims at inferring the correspondence between projected depth image and RGB image to learn the underlying geometry of 2D-3D calibration. Thus, the proposed calibration model achieves a good generalization ability to adapt to unknown initial calibration error ranges, and other 3D LiDAR and 2D camera pairs with different intrinsic parameters from the training dataset. Extensive experiments have demonstrated that our CalibRCNN can achieve stateof-the-art accuracy by comparison with other CNN based methods.

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“…This method needs to input the transformation relationship among a large amount of images in advance. Jiang et al [33] provided an online calibration method using road lines. They assumed that there are three lines which can be detected by both the camera and LiDAR on the road.…”

Section: Related Workmentioning

confidence: 99%

LiDAR-Camera Calibration Using Line Correspondences

Bai

Jiang

2020

Sensors

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In this paper, we introduce a novel approach to estimate the extrinsic parameters between a LiDAR and a camera. Our method is based on line correspondences between the LiDAR point clouds and camera images. We solve the rotation matrix with 3D–2D infinity point pairs extracted from parallel lines. Then, the translation vector can be solved based on the point-on-line constraint. Different from other target-based methods, this method can be performed simply without preparing specific calibration objects because parallel lines are commonly presented in the environment. We validate our algorithm on both simulated and real data. Error analysis shows that our method can perform well in terms of robustness and accuracy.

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Line Feature Based Extrinsic Calibration of LiDAR and Camera

Cited by 25 publications

References 10 publications

Improvements to Target-Based 3D LiDAR to Camera Calibration

Improvements to Target-Based 3D LiDAR to Camera Calibration

CalibRCNN: Calibrating Camera and LiDAR by Recurrent Convolutional Neural Network and Geometric Constraints

LiDAR-Camera Calibration Using Line Correspondences

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