Line-based extrinsic calibration of range and image sensors

Moghadam, Peyman; Bosse, Michael; Zlot, Robert

doi:10.1109/icra.2013.6631095

Cited by 119 publications

(77 citation statements)

References 8 publications

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“…Mirzaei et al [9] proposed an algorithm for the simultaneous estimation of the intrinsic parameters of a 3D LI-DAR and extrinsic calibration with respect to camera. P. Moghadam et al [10] uses line segment for the extrinsic calibration of range and image sensors that requires no external artificial targets. Multiple planes are also used in the extrinsic calibration of range sensor and camera [11].…”

Section: Introductionmentioning

confidence: 99%

Improved algorithm for the extrinsic calibration of a camera and laser range finder using 3D-3D correspondences

2015

Int. J. Control Autom. Syst.

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Extrinsic calibration between a camera and laser range finder (LRF) is required to integrate information from both sensor. In this paper, we propose an improved algorithm of our earlier work [1] for the extrinsic calibration of a camera and laser range finder. We improve our previous algorithm in two directions. First, control points on laser range finder's data are obtained by finding the crossing point of two fitted lines where previous algorithm directly used laser range finder's data. It can reduce the effect of noisy measurement of laser range finder. Second, difference of distance on calibration plane is used as cost where previous algorithm used matching portion. Proposed algorithm gives an improved result than previous one. It is verified thorough experimental results.

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Section: Introductionmentioning

confidence: 99%

Improved algorithm for the extrinsic calibration of a camera and laser range finder using 3D-3D correspondences

2015

Int. J. Control Autom. Syst.

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“…Moghadam et al [18] estimate the spatial relation from line segments that can be detected both in the point cloud and in the camera image. Taylor and Nieto [19] find the spatial relation by maximizing the mutual information between the camera image and a virtual image, which is colored according to the direction of the point cloud's normals.…”

Section: Related Workmentioning

confidence: 99%

Accurate laser scanner to camera calibration with application to range sensor evaluation

Fuersattel

Plank

Maier

et al. 2017

IPSJ T Comput Vis Appl

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Multi-modal sensory data plays an important role in many computer vision and robotics tasks. One popular multi-modal pair is cameras and laser scanners. To overlay and jointly use the data from both modalities, it is necessary to calibrate the sensors, i.e., to obtain the spatial relation between the sensors. Computing such a calibration is challenging as both sensors provide quite different data: cameras yield color or brightness information, laser scanners yield 3-D points. However, several laser scanners additionally provide reflectances, which turn out to make calibration to a camera well feasible. To this end, we first estimate a rough alignment of the coordinate systems of both modalities. Then, we use the laser scanner reflectances to compute a virtual image of the scene. Stereo calibration on the virtual image and the camera image are then used to compute a refined, high-accuracy calibration. It is encouraging that the accuracies in our experiments are comparable to camera-camera stereo setups and outperform another of other target-based calibration approach. This shows that the proposed algorithm reliably integrates the point cloud with the intensity image. As an example application, we use the calibration results to obtain ground-truth distance images for range cameras. Furthermore, we utilize this data to investigate the accuracy of the Microsoft Kinect V2 time-of-flight and the Intel RealSense R200 structured light camera.

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“…However, this approach cannot apply directly to range sensors without associated reflectivity information. Recently, Moghadam et al [21] proposes an extrinsic range-image calibration technique which utilizes strong line-to-line geometric constraints to jointly estimate the 6DoF transformation between the coordinate systems. The method requires no special calibration pattern and is not limited to any particular types or configurations of range and imaging sensors.…”

Section: Introductionmentioning

confidence: 99%

Augmented Feature Point Initialization Method for Vision/Lidar Aided 6-DoF Bearing-Only Inertial SLAM

Yun¹,

Lee²,

Kim³

et al. 2016

Journal of Electrical Engineering and Technology

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-This study proposes a novel feature point initialization method in order to improve the accuracy of feature point positions by fusing a vision sensor and a lidar. The initialization is a process that determines three dimensional positions of feature points through two dimensional image data, which has a direct influence on performance of a 6-DoF bearing-only SLAM. Prior to the initialization, an extrinsic calibration method which estimates rotational and translational relationships between a vision sensor and lidar using multiple calibration tools was employed, then the feature point initialization method based on the estimated extrinsic calibration parameters was presented. In this process, in order to improve performance of the accuracy of the initialized feature points, an iterative automatic scaling parameter tuning technique was presented. The validity of the proposed feature point initialization method was verified in a 6-DoF bearing-only SLAM framework through an indoor and outdoor tests that compare estimation performance with the previous initialization method.

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Line-based extrinsic calibration of range and image sensors

Cited by 119 publications

References 8 publications

Improved algorithm for the extrinsic calibration of a camera and laser range finder using 3D-3D correspondences

Improved algorithm for the extrinsic calibration of a camera and laser range finder using 3D-3D correspondences

Accurate laser scanner to camera calibration with application to range sensor evaluation

Augmented Feature Point Initialization Method for Vision/Lidar Aided 6-DoF Bearing-Only Inertial SLAM

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