On task-oriented criteria for configurations selection in robot calibration

Carrillo, Henry; Birbach, Oliver; Täubig, Holger; Bäuml, Berthold; Frese, Udo; Castellanos, José A.

doi:10.1109/icra.2013.6631090

Cited by 16 publications

(15 citation statements)

References 18 publications

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“…Most similar to our approach, Carrillo et al [11] recently experimentally compared different criteria for the selection of measurement configurations for the calibration of a humanoid's upper body. The authors conclude that for greedyselection algorithms, determinant-based criteria should be preferred.…”

Section: Related Workmentioning

confidence: 80%

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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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Section: Related Workmentioning

confidence: 80%

“…Hence, we seek to reduce the variance or uncertainty of the parameters. To this end, we evaluated the following observability indices [11,14]:…”

Section: B Selecting a Subset Of Valuable Configurationsmentioning

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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“…(16)- (17) it is apparant that the relative translation between both sensors is poorly identified in the direction of the rotation axis. As the robot's platform only possibility to excite angular velocities is through rotation on the floor, the difference in height between both inertial sensors would be poorly determined.…”

Section: B Identifiabilitymentioning

confidence: 98%

Calibrating a pair of inertial sensors at opposite ends of an imperfect kinematic chain

Birbach¹,

Bäuml²

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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This paper addresses the problem of determining the poses of a pair of inertial sensors mounted at the opposite ends of an imperfect kinematic chain. Due to constraints during design, robots may not be equipped with the required sensors to intrinsically recover the kinematic state up to the required precision. To recover the unknown state using a pair of inertial sensors their relative pose to the opposite ends must be known. We propose an approach to calibrate these unknown relationships in a straightforward and methodically sound way while considering any existing inaccuracies in the kinematic chain. To obtain the desired parameters the calibration problem is formulated as a least squares batch-optimization problem. The proposed approach is integrated on DLR's humanoid robot Agile Justin to determine the pair of inertial sensors mounted at the opposite ends of the torso/head chain and further experimentally validated.

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“…Recently there has also been work focusing specifically on the problem of selecting a set of configurations that maximizes the accuracy of the calibration while minimizing the calibration time [7], [11], [12]. These works do not consider on-line calibration and instead generate a batch of configurations that is checked for marker visibility and collision-free, and then subsample this batch before executing it on the robot.…”

Section: Related Workmentioning

confidence: 99%

“…At each time instance, the newly arriving sample is evaluated against all other samples in the configuration set in order to greedily maximize an observability index. In accordance with [7], [12] we use the following index:…”

Section: On-line Calibrationmentioning

confidence: 99%

Integrated on-line robot-camera calibration and object pose estimation

Pauwels

Kragić

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-We present a novel on-line approach for extrinsic robot-camera calibration, a process often referred to as handeye calibration, that uses object pose estimates from a real-time model-based tracking approach. While off-line calibration has seen much progress recently due to the incorporation of bundle adjustment techniques, on-line calibration still remains a largely open problem. Since we update the calibration in each frame, the improvements can be incorporated immediately in the pose estimation itself to facilitate object tracking. Our method does not require the camera to observe the robot or to have markers at certain fixed locations on the robot. To comply with a limited computational budget, it maintains a fixed size configuration set of samples. This set is updated each frame in order to maximize an observability criterion. We show that a set of size 20 is sufficient in real-world scenarios with static and actuated cameras. With this set size, only 100 microseconds are required to update the calibration in each frame, and we typically achieve accurate robot-camera calibration in 10 to 20 seconds. Together, these characteristics enable the incorporation of calibration in normal task execution. I. INTRODUCTIONTo enable a robot to interact with objects observed by a camera, and to learn from these interactions, it is invaluable to know the pose of the camera in the robot's reference frame. This allows for the robot to situate perceived objects with respect to itself, as illustrated in Fig. 1. The camera pose can be obtained using a robot-camera calibration procedure, often referred to as hand-eye calibration. Typically such calibration requires a time-intensive procedure in which first a fixed sequence of robot configurations are determined and executed while a video is recorded. This video is then processed off-line with a vision-based pose estimation method in order to detect the pose of a calibration object or markers attached to the robot. Finally a batch optimization is performed to determine the calibration parameters. The hand-eye calibration problem is not simply a pose estimation problem since both the position or pose of the calibration object and the camera pose with respect to the robot are unknown. So as a side product of the calibration, the object's pose or position in world coordinates is obtained as well.In many real-world situations, the camera pose relative to the robot may change over time due to changing temperature, humidity, or due to a contact or collision with the environment. It may also be necessary to quickly add

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On task-oriented criteria for configurations selection in robot calibration

Cited by 16 publications

References 18 publications

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

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

Calibrating a pair of inertial sensors at opposite ends of an imperfect kinematic chain

Integrated on-line robot-camera calibration and object pose estimation

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