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      A Convenient Calibration Method for LRF-Camera Combination Systems Based on a Checkerboard

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          Abstract

          In this paper, a simple and easy high-precision calibration method is proposed for the LRF-camera combined measurement system which is widely used at present. This method can be applied not only to mainstream 2D and 3D LRF-cameras, but also to calibrate newly developed 1D LRF-camera combined systems. It only needs a calibration board to record at least three sets of data. First, the camera parameters and distortion coefficients are decoupled by the distortion center. Then, the spatial coordinates of laser spots are solved using line and plane constraints, and the estimation of LRF-camera extrinsic parameters is realized. In addition, we establish a cost function for optimizing the system. Finally, the calibration accuracy and characteristics of the method are analyzed through simulation experiments, and the validity of the method is verified through the calibration of a real system.

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          Most cited references 32

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          Real-time large-scale dense RGB-D SLAM with volumetric fusion

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            Error modelling, calibration and analysis of an AM–CW terrestrial laser scanner system

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              A minimal solution for the extrinsic calibration of a camera and a laser-rangefinder.

              This paper presents a new algorithm for the extrinsic calibration of a perspective camera and an invisible 2D laser-rangefinder (LRF). The calibration is achieved by freely moving a checkerboard pattern in order to obtain plane poses in camera coordinates and depth readings in the LRF reference frame. The problem of estimating the rigid displacement between the two sensors is formulated as one of registering a set of planes and lines in the 3D space. It is proven for the first time that the alignment of three plane-line correspondences has at most eight solutions that can be determined by solving a standard p3p problem and a linear system of equations. This leads to a minimal closed-form solution for the extrinsic calibration that can be used as hypothesis generator in a RANSAC paradigm. Our calibration approach is validated through simulation and real experiments that show the superiority with respect to the current state-of-the-art method requiring a minimum of five input planes.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                15 March 2019
                March 2019
                : 19
                : 6
                Affiliations
                [1 ]Institute of Optics and Electronics of Chinese Academy of Sciences, Chengdu 610209, China; zhangzhuang14@ 123456mails.ucas.ac.cn (Z.Z.); leh@ 123456ioe.ac.cn (E.L.); yankunioe@ 123456163.com (K.Y.); MYB_IOE@ 123456163.com (Y.M.)
                [2 ]University of Chinese Academy of Sciences, Beijing 100149, China
                Author notes
                [* ]Correspondence: zrj0515@ 123456163.com ; Tel.: +86-155-3000-3288
                [†]

                These authors contributed equally to this work.

                Article
                sensors-19-01315
                10.3390/s19061315
                6470948
                30884756
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                Categories
                Article

                Biomedical engineering

                lrf, camera calibration, extrinsic calibration, sensors combination

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