Bayesian calibration of the hand-eye kinematics of an anthropomorphic robot

Hubert, Uwe; Stückler, Jörg; Behnke, Sven

doi:10.1109/humanoids.2012.6651584

Cited by 10 publications

(6 citation statements)

References 10 publications

(12 reference statements)

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“…Assuming a priori camera calibration, extrinsic parameters relative to the neck are determined as well as either optimizing the joint-angle offset or the involved Denavit-Hartenberg (DH) parameters. In Hubert et al (2012), DH parameters and joint gear reductions of the hand-eye kinematic chain are refined using a maximum a posteriori (MAP) estimator incorporating a priori knowledge of the hand-eye kinematic chain for robust parameter estimation fed by observations of a checkerboard target gripped at the robot's end-effector.…”

Section: Humanoid Robot Calibrationmentioning

confidence: 99%

Rapid calibration of a multi-sensorial humanoid’s upper body: An automatic and self-contained approach

Birbach¹,

Frese

Bäuml³

2014

The International Journal of Robotics Research

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This paper addresses the problem of calibrating a pair of cameras, a Microsoft Kinect sensor and an inertial measurement unit (IMU) mounted at the head of a humanoid robot with respect to its kinematic chain. As complex manipulation tasks require an accurate interplay of all involved sensors, the quality of calibration is crucial for the outcome of the intended tasks. Typical procedures for calibrating are often time-consuming, involve multiple people overseeing a series of subsequent calibration steps and require external tools. We therefore propose to auto-calibrate all sensors in a single, completely automatic and self-contained procedure, i.e. without a calibration plate. By automatically detecting a single point feature on each wrist while moving the robot's head, the stereo cameras', the Kinect's infrared camera's intrinsic and extrinsic and an IMU's extrinsic parameters are calibrated while considering the arm joint elasticities and joint angle offsets. All parameters are obtained by formulating the calibration problem as a single least-squares batch-optimization problem. The procedure is integrated on DLR's humanoid robot Agile Justin allowing to obtain an accurate calibration in around 5 minutes by simply ''pushing a button''. The proposed approach is experimentally validated by means of standard metrics of the calibration errors.

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Section: Humanoid Robot Calibrationmentioning

confidence: 99%

Rapid calibration of a multi-sensorial humanoid’s upper body: An automatic and self-contained approach

Birbach¹,

Frese

Bäuml³

2014

The International Journal of Robotics Research

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“…The authors use a single point marker located on each of the arms' wrists. Similarly, Hubert et al [7] applied a maximum a priori approach (MAP) for estimating the parameters of hand-eye kinematics of a humanoid robot. The prior is assumed to be normal distributed with an empirically determined variance.…”

Section: Related Workmentioning

confidence: 99%

Whole-body self-calibration via graph-optimization and automatic configuration selection

Maier

Wrobel

Bennewitz

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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In this paper, we present a novel approach to accurately calibrate the kinematic model of a humanoid based on observations of its monocular camera. Our technique estimates the parameters of the complete model, consisting of the joint angle offsets of the whole body including the legs, as well as the camera extrinsic and intrinsic parameters. We cast the parameter estimation as a least-squares optimization problem. In the error function, we consider the residuals between camera observations of end-effector markers and their projections into the image based on the estimate of the calibration parameters. Furthermore, we developed an approach to automatically select a subset of configurations for the calibration process that yields a good trade-off between the number of observations and accuracy. As the experiments with a Nao humanoid show, we achieve an accurate calibration for this low-cost platform. Further, our approach to configuration selection yields substantially better optimization results compared to randomly chosen viable configurations. Hence, our system only requires a reduced number of configurations to achieve accurate results. Our optimization is general and the implementation, which is available online, can easily be applied to different humanoids.

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“…Fanello et al [4] had the robot observe its fingertip and learned essentially a single transformation only to account for the discrepancy between forward kinematics of the arm and the projection of the finger into the cameras. Other works dealing with hand-eye kinematic chains are [10], [11].…”

Section: Related Workmentioning

confidence: 99%

Robot Self-Calibration Using Multiple Kinematic Chains—A Simulation Study on the iCub Humanoid Robot

Štěpánová

Pajdla

Hoffmann

2019

IEEE Robot. Autom. Lett.

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Proper calibration is key for the performance of every robot. Pushed by societal needs and economic opportunities, robots are leaving fixed factory floors and are deployed in more versatile ways both in industry and outside, which increases the need for automated calibration procedures. At the same time, advances in sensor technology make affordable but increasingly accurate devices such as RGB-D and tactile sensors available, making it possible to perform automated self-contained calibration relying on redundant information in these sensory streams. In this work, we take the example of a humanoid robot with a stereo camera system and force-sensitive end-effectors and quantitatively compare the performance of kinematic calibration by employing different combinations of intersecting kinematic chains-either through self-observation or self-touch.

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Bayesian calibration of the hand-eye kinematics of an anthropomorphic robot

Cited by 10 publications

References 10 publications

Rapid calibration of a multi-sensorial humanoid’s upper body: An automatic and self-contained approach

Rapid calibration of a multi-sensorial humanoid’s upper body: An automatic and self-contained approach

Whole-body self-calibration via graph-optimization and automatic configuration selection

Robot Self-Calibration Using Multiple Kinematic Chains—A Simulation Study on the iCub Humanoid Robot

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