Extrinsic Calibration of 2-D Lidars Using Two Orthogonal Planes

Choi, Dong‐Geol; Bok, Yunsu; Kim, Jun-Sik; Kweon, In So

doi:10.1109/tro.2015.2502860

Cited by 48 publications

(34 citation statements)

References 21 publications

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“…The maximum scan rate of the system is about 20 Hz. The system is fully calibrated: camera intrinsic parameters using [ 30 ], lidar to camera [ 31 , 32 ] and between two lidars [ 33 ].…”

Section: Resultsmentioning

confidence: 99%

Structure-From-Motion in 3D Space Using 2D Lidars

Choi

Bok

Kim

et al. 2017

Sensors

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This paper presents a novel structure-from-motion methodology using 2D lidars (Light Detection And Ranging). In 3D space, 2D lidars do not provide sufficient information for pose estimation. For this reason, additional sensors have been used along with the lidar measurement. In this paper, we use a sensor system that consists of only 2D lidars, without any additional sensors. We propose a new method of estimating both the 6D pose of the system and the surrounding 3D structures. We compute the pose of the system using line segments of scan data and their corresponding planes. After discarding the outliers, both the pose and the 3D structures are refined via nonlinear optimization. Experiments with both synthetic and real data show the accuracy and robustness of the proposed method.

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“…The maximum scan rate of the system is about 20 Hz. The system is fully calibrated: camera intrinsic parameters using [ 30 ], lidar to camera [ 31 , 32 ] and between two lidars [ 33 ].…”

Section: Resultsmentioning

confidence: 99%

Structure-From-Motion in 3D Space Using 2D Lidars

Choi

Bok

Kim

et al. 2017

Sensors

Self Cite

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“…Gao et al [8] proposed a multi-LiDAR calibration method using point constraints from the retro-reflective targets placed in scenes. Choi et al [9] determined spatial offset of dual 2D LiDARs by employing the appearance of two orthogonal planes. For calibrating a LiDAR with a camera.…”

Section: A Appearance-based Approachesmentioning

confidence: 99%

“…There are surprisingly few discussions on calibrating multiple 3D Li-DARs. Moreover, the majority of current approaches involve one or more of the following assumptions: prior knowledge about the structure of environments [7], usage of additional sensors [8], and user-provided initial values of the extrinsic 1 parameters [9]. Inspired by the progress on the hand-eye calibration, we find that the extrinsic parameters can be directly recovered from individual motions provided by each LiDAR.…”

Section: Introduction a Motivationmentioning

confidence: 99%

Automatic Calibration of Multiple 3D LiDARs in Urban Environments

Jiao

Liao

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Multiple LiDARs have progressively emerged on autonomous vehicles for rendering a wide field of view and dense measurements. However, the lack of precise calibration negatively affects their potential applications in localization and perception systems. In this paper, we propose a novel system that enables automatic multi-LiDAR calibration without any calibration target, prior environmental information, and initial values of the extrinsic parameters. Our approach starts with a hand-eye calibration for automatic initialization by aligning the estimated motions of each sensor. The resulting parameters are then refined with an appearance-based method by minimizing a cost function constructed from point-plane correspondences. Experimental results on simulated and real-world data sets demonstrate the reliability and accuracy of our calibration approach. The proposed approach can calibrate a multi-LiDAR system with the rotation and translation errors less than 0.04 [rad] and 0.1 [m] respectively for a mobile platform.

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“…Rodriguez et al [11] presented an extrinsic and intrinsic calibration approach by circle based calibration target. Beside, Choi [12] used two orthogonal planes; Aliakbarpour [13] used a pattern and IMU sensor together to jointly reduce the needed number of points. Gonget al [14] used an arbitrary trihedron to assist the calibration.…”

Section: B Challengesmentioning

confidence: 99%

Extrinsic Calibration of 3D Range Finder and Camera without Auxiliary Object or Human Intervention

Liao

Liu

2019

2019 IEEE International Conference on Real-Time Computing and Robotics (RCAR)

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Fusion of heterogeneous extroceptive sensors is the most effient and effective way to representing the environment precisely, as it overcomes various defects of each homogeneous sensor. The rigid transformation (aka. extrinsic parameters) of heterogeneous sensory systems should be available before precisely fusing the multisensor information. Researchers have proposed several approaches to estimating the extrinsic parameters. These approaches require either auxiliary objects, like chessboards, or extra help from human to select correspondences. In this paper, we proposed a novel extrinsic calibration approach for the extrinsic calibration of range and image sensors. As far as we know, it is the first automatic approach with no requirement of auxiliary objects or any human interventions.First, we estimate the initial extrinsic parameters from the individual motion of the range finder and the camera. Then we extract lines in the image and point-cloud pairs, to refine the line feature associations by the initial extrinsic parameters.At the end, we discussed the degenerate case which may lead to the algorithm failure and validate our approach by simulation. The results indicate high-precision extrinsic calibration results against the ground-truth.

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Extrinsic Calibration of 2-D Lidars Using Two Orthogonal Planes

Cited by 48 publications

References 21 publications

Structure-From-Motion in 3D Space Using 2D Lidars

Structure-From-Motion in 3D Space Using 2D Lidars

Automatic Calibration of Multiple 3D LiDARs in Urban Environments

Extrinsic Calibration of 3D Range Finder and Camera without Auxiliary Object or Human Intervention

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