Targetless Calibration of a Lidar - Perspective Camera Pair

Tamás, Levente; Kató, Zoltán

doi:10.1109/iccvw.2013.92

Cited by 40 publications

(30 citation statements)

References 27 publications

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“…They project the line from the point cloud onto the image and find the two projected points that are closest to the endpoints of the line segment in the image; the distance between the points is then minimized by a numerical gradient. Tamas and Kato () extract planar regions (3D features) in both measurements by using the region growing algorithm. Then, the numerical Levenberg–Marquardt algorithm optimizes the overlapping area of the regions.…”

Section: Related Workmentioning

confidence: 99%

Automatic targetless camera–LIDAR calibration by aligning edge with Gaussian mixture model

Kang

Doh

2019

Journal of Field Robotics

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This paper presents a calibration algorithm that does not require an artificial target object to precisely estimate a rigid‐body transformation between a camera and a light detection and ranging (LIDAR) sensor. The proposed algorithm estimates calibration parameters by minimizing a cost function that evaluates the edge alignment between two sensor measurements. In particular, the proposed cost function is constructed using a projection model‐based many‐to‐many correspondence of the edges to fully exploit measurements with different densities (dense photometry and sparse geometry). The alignment of the many‐to‐many correspondence is represented using the Gaussian mixture model (GMM) framework. Here, each component of the GMM, including weight, displacement, and standard deviation, is derived to suitably capture the intensity, location, and influential range of the edge measurements, respectively. The derived cost function is optimized by the gradient descent method with an analytical derivative. A coarse‐to‐fine scheme is also applied by gradually decreasing the standard deviation of the GMM to enhance the robustness of the algorithm. Extensive indoor and outdoor experiments validate the claim that the proposed GMM strategy improves the performance of the proposed algorithm. The experimental results also show that the proposed algorithm outperforms previous methods in terms of precision and accuracy by providing calibration parameters of standard deviations less than 0.6° and 2.1 cm with a reprojection error of 1.78 for a 2.1‐megapixel image (2,048 × 1,024) in the best case.

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Section: Related Workmentioning

confidence: 99%

Automatic targetless camera–LIDAR calibration by aligning edge with Gaussian mixture model

Kang

Doh

2019

Journal of Field Robotics

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“…Most state-of-the-art approaches handle the 3D-2D registration between a camera and a depth sensor by using special calibration targets [1], [2], [3]. Other semi-automatic methods extract human-selected 3D and 2D shapes from both sensors which are then aligned [12], [13]. The mentioned methods achieve excellent results and can therefore be used for a suitable initial calibration.…”

Section: Related Workmentioning

confidence: 99%

“…The mentioned methods achieve excellent results and can therefore be used for a suitable initial calibration. However, they are either time consuming [12], [13], [2], [3]. or require a controlled environment [1].…”

Section: Related Workmentioning

confidence: 99%

RegNet: Multimodal sensor registration using deep neural networks

Schneider

Piewak

Stiller

et al. 2017

2017 IEEE Intelligent Vehicles Symposium (IV)

152

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Abstract-In this paper, we present RegNet, the first deep convolutional neural network (CNN) to infer a 6 degrees of freedom (DOF) extrinsic calibration between multimodal sensors, exemplified using a scanning LiDAR and a monocular camera. Compared to existing approaches, RegNet casts all three conventional calibration steps (feature extraction, feature matching and global regression) into a single real-time capable CNN. Our method does not require any human interaction and bridges the gap between classical offline and target-less online calibration approaches as it provides both a stable initial estimation as well as a continuous online correction of the extrinsic parameters. During training we randomly decalibrate our system in order to train RegNet to infer the correspondence between projected depth measurements and RGB image and finally regress the extrinsic calibration. Additionally, with an iterative execution of multiple CNNs, that are trained on different magnitudes of decalibration, our approach compares favorably to state-of-the-art methods in terms of a mean calibration error of 0.28• for the rotational and 6 cm for the translation components even for large decalibrations up to 1.5 m and 20• .

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“…its intrinsic parameters (a 0 , a 2 , a 3 , a 4 ) are known) and the relative pose (R, t) has to be estimated. Inspired by [25], we will reformulate pose estimation as a 2D-3D shape alignment problem. Our solution is based on the correspondence-less 2D shape registration approach of Domokos et al [2], where non-linear shape deformations are recovered via the solution of a nonlinear system of equations.…”

Section: Omnidirectional Camera Modelmentioning

confidence: 99%

Relative Pose Estimation and Fusion of Omnidirectional and Lidar Cameras

Tamás

Fröhlich

Kató

2015

Computer Vision - ECCV 2014 Workshops

Self Cite

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Abstract. This paper presents a novel approach for the extrinsic parameter estimation of omnidirectional cameras with respect to a 3D Lidar coordinate frame. The method works without specific setup and calibration targets, using only a pair of 2D-3D data. Pose estimation is formulated as a 2D-3D nonlinear shape registration task which is solved without point correspondences or complex similarity metrics. It relies on a set of corresponding regions, and pose parameters are obtained by solving a small system of nonlinear equations. The efficiency and robustness of the proposed method was confirmed on both synthetic and real data in urban environment.

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Targetless Calibration of a Lidar - Perspective Camera Pair

Cited by 40 publications

References 27 publications

Automatic targetless camera–LIDAR calibration by aligning edge with Gaussian mixture model

Automatic targetless camera–LIDAR calibration by aligning edge with Gaussian mixture model

RegNet: Multimodal sensor registration using deep neural networks

Relative Pose Estimation and Fusion of Omnidirectional and Lidar Cameras

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