An overview of robot-sensor calibration methods for evaluation of perception systems

Shah, Mili; Eastman, Jason D.; Hong, Tsai Hong

doi:10.1145/2393091.2393095

Cited by 76 publications

(40 citation statements)

References 20 publications

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“…There are many methods to solve Equations (11) and (12). According to [23], the method proposed by Tsai et al [24] exhibits the best performance. We therefore chose to use this method.…”

Section: Pre-processing Step 1: Lidar-imu Hand-eye Calibrationmentioning

confidence: 99%

IMU-Aided High-Frequency Lidar Odometry for Autonomous Driving

Xue

Dai

2019

Applied Sciences

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For autonomous driving, it is important to obtain precise and high-frequency localization information. This paper proposes a novel method in which the Inertial Measurement Unit (IMU), wheel encoder, and lidar odometry are utilized together to estimate the ego-motion of an unmanned ground vehicle. The IMU is fused with the wheel encoder to obtain the motion prior, and it is involved in three levels of the lidar odometry: Firstly, we use the IMU information to rectify the intra-frame distortion of the lidar scan, which is caused by the vehicle’s own movement; secondly, the IMU provides a better initial guess for the lidar odometry; and thirdly, the IMU is fused with the lidar odometry in an Extended Kalman filter framework. In addition, an efficient method for hand–eye calibration between the IMU and the lidar is proposed. To evaluate the performance of our method, extensive experiments are performed and our system can output stable, accurate, and high-frequency localization results in diverse environment without any prior information.

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“…There are many methods to solve Equations (11) and (12). According to [23], the method proposed by Tsai et al [24] exhibits the best performance. We therefore chose to use this method.…”

Section: Pre-processing Step 1: Lidar-imu Hand-eye Calibrationmentioning

confidence: 99%

IMU-Aided High-Frequency Lidar Odometry for Autonomous Driving

Xue

Dai

2019

Applied Sciences

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“…17is rewritten as follows: (19) Several methods exist to solve the hand-eye calibration problem with different optimization processes. A general taxonomy of the classical approaches is provided in article [38] and in the focus of our contribution, they can be split into two classes: non-unified and unified methods. Using the decomposition (18), the non-unified methods separately estimate the translation t X and the rotation R X .…”

Section: Background and Work Related To Hand-eye Calibrationmentioning

confidence: 99%

Automated thermal 3D reconstruction based on a robot equipped with uncalibrated infrared stereovision cameras

Sentenac

Bugarin

Ducarouge

et al. 2018

Advanced Engineering Informatics

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In many industrial sectors, Non Destructive Testing (NDT) methods are used for the thermomechanical analysis of parts in assemblies of engines or reactors or for the control of metal forming processes. This article suggests an automated multi-view approach for the thermal D 3 reconstruction required in order to compute D 3 surface temperature models. This approach is based only on infrared cameras mounted on a Cartesian robot. The low resolution of these cameras associated to a lack of texture to infrared images require to use a global approach based first on an uncalibrated rectification and then on the simultaneous execution, in a single step, of the dense D 3 reconstruction and of an extended self-calibration. The uncalibrated rectification is based on an optimization process under constraints which calculates the homographies without prior calculation of the Fundamental Matrix and which minimizes the projective deformations between the initial images and the rectified ones. The extended self-calibration estimates both the parameters of virtual cameras that could provide the rectified images directly, and the parameters of the robot. It is based on two criteria evaluated according to the noise level of the infrared images. This global approach is validated through the reconstruction of a hot object against a reference reconstruction acquired by a D 3 scanner.

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“…In order to relate the measurements made by a camera mounted on a robotic gripper to the tool's coordinate frame, a homogeneous transformation from the tool to the camera needs to be determined. This problem usually called robot-sensor calibration and it is divided into two forms: AX = XB and AX = YB [1]. Figure 1 illustrates schematically the geometry of AX = YB robot-sensor calibration, which defines a problem in which the relationship between the world frame and robot base frame is unknown in addition to the unknown relationship between the robot tool and the rigidly mounted sensor, and it has been well studied in [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Calibration-Free Robot-Sensor Calibration approach based on Second-Order Cone Programming

Lü

Dong

et al. 2018

MATEC Web Conf.

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In order to overcome the restrictions of traditional robot-sensor calibration method which solve the tool-camera transformation and robot-world transformation rely on calibration target, a calibration-free approach that solve the robot-sensor calibration problem of the form AX = YB based on Second-Order Cone Programming is proposed. First, a Structure-From-Motion approach was used to recover the camera motion matrix up to scaling. Then rotation and translation matrix in calibration equation were parameterized by dual quaternion theory. Finally, the Second-Order Cone Programming method was used to simultaneously solve the optimal solution of camera motion matrix scale factor, the robot-world and hand-eye relation. The experimental results indicate that the calibration precision of rotation relative error is 3.998% and the translation relative error is 0.117% in defect of calibration target as 3D benchmark. Compared with similar methods, the proposed method can effectively improve the calibration accuracy of the robot-world and hand-eye relation, and extend the application field of robot-sensor calibration method.

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An overview of robot-sensor calibration methods for evaluation of perception systems

Cited by 76 publications

References 20 publications

IMU-Aided High-Frequency Lidar Odometry for Autonomous Driving

IMU-Aided High-Frequency Lidar Odometry for Autonomous Driving

Automated thermal 3D reconstruction based on a robot equipped with uncalibrated infrared stereovision cameras

Calibration-Free Robot-Sensor Calibration approach based on Second-Order Cone Programming

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