An automatic calibration approach for a multi-camera-robot system

Kroeger, Ole; Huegle, Johannes; Niebuhr, Carsten

doi:10.1109/etfa.2019.8869522

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…The calibration of proprioceptive and exteroceptive sensors is often considered a spatial problem, where intrinsic properties are calculated separately from the extrinsic, and the main concern is in determining a translation vector and rotation matrix. This manifests in work where the goal is to determine these extrinsic parameters either through online or offline methods [15], [16]. However, the calibration problem is not purely spatial, but rather temporospatial as it is necessary to know the location, orientation, and time which a measurement was taken in order to optimally include that measurement into a localization filter.…”

Section: Related Workmentioning

confidence: 99%

Online Multi Camera-IMU Calibration

Hartzer¹,

Saripalli²

2022

Preprint

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Visual-inertial navigation systems are powerful in their ability to accurately estimate localization of mobile systems within complex environments that preclude the use of global navigation satellite systems. However, these navigation systems are reliant on accurate and up-to-date temporospatial calibrations of the sensors being used. As such, online estimators for these parameters are useful in resilient systems. This paper presents an extension to existing Kalman Filter based frameworks for estimating and calibrating the extrinsic parameters of multi-camera IMU systems. In addition to extending the filter framework to include multiple camera sensors, the measurement model was reformulated to make use of measurement data that is typically made available in fiducial detection software. A secondary filter layer was used to estimate time translation parameters without closed-loop feedback of sensor data. Experimental calibration results, including the use of cameras with non-overlapping fields of view, were used to validate the stability and accuracy of the filter formulation when compared to offline methods. Finally the generalized filter code has been open-sourced and is available online.

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Section: Related Workmentioning

confidence: 99%

Online Multi Camera-IMU Calibration

Hartzer¹,

Saripalli²

2022

Preprint

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“…Camera network calibration is required for several applications, including multicamera navigation systems [12], people-tracking within camera networks [13], and surveillance systems [14]. Camera network calibration is also critical in robotic scenarios [15,16], especially when dealing with a robotic workcell composed of a robot arm surrounded by a camera network installed to monitor the workcell area [5,17,18]. In such cases, it is essential to provide the robot with accurate information about its working environment.…”

Section: Introductionmentioning

confidence: 99%

METRIC—Multi-Eye to Robot Indoor Calibration Dataset

2023

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Multi-camera systems are an effective solution for perceiving large areas or complex scenarios with many occlusions. In such a setup, an accurate camera network calibration is crucial in order to localize scene elements with respect to a single reference frame shared by all the viewpoints of the network. This is particularly important in applications such as object detection and people tracking. Multi-camera calibration is a critical requirement also in several robotics scenarios, particularly those involving a robotic workcell equipped with a manipulator surrounded by multiple sensors. Within this scenario, the robot-world hand-eye calibration is an additional crucial element for determining the exact position of each camera with respect to the robot, in order to provide information about the surrounding workspace directly to the manipulator. Despite the importance of the calibration process in the two scenarios outlined above, namely (i) a camera network, and (ii) a camera network with a robot, there is a lack of standard datasets available in the literature to evaluate and compare calibration methods. Moreover they are usually treated separately and tested on dedicated setups. In this paper, we propose a general standard dataset acquired in a robotic workcell where calibration methods can be evaluated in two use cases: camera network calibration and robot-world hand-eye calibration. The Multi-Eye To Robot Indoor Calibration (METRIC) dataset consists of over 10,000 synthetic and real images of ChAruCo and checkerboard patterns, each one rigidly attached to the robot end-effector, which was moved in front of four cameras surrounding the manipulator from different viewpoints during the image acquisition. The real images in the dataset includes several multi-view image sets captured by three different types of sensor networks: Microsoft Kinect V2, Intel RealSense Depth D455 and Intel RealSense Lidar L515, to evaluate their advantages and disadvantages for calibration. Furthermore, in order to accurately analyze the effect of camera-robot distance on calibration, we acquired a comprehensive synthetic dataset, with related ground truth, with three different camera network setups corresponding to three levels of calibration difficulty depending on the cell size. An additional contribution of this work is to provide a comprehensive evaluation of state-of-the-art calibration methods using our dataset, highlighting their strengths and weaknesses, in order to outline two benchmarks for the two aforementioned use cases.

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“…There are methods for 2D camera calibration already presented, and they can be divided into two main approaches. The eye-on-hand calibration, wherein the camera is mounted on the robot and a calibration plate is static, and the eye-on-base method with the calibration marker mounted on the robot with static cameras around [19]. There are also Robot Operating System (ROS) packages [20,21] providing tools for 2D or 3D camera calibration using these two methods.…”

Section: Introductionmentioning

confidence: 99%

Camera-Based Method for Identification of the Layout of a Robotic Workcell

et al. 2020

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In this paper, a new method for the calibration of robotic cell components is presented and demonstrated by identification of an industrial robotic manipulator’s base and end-effector frames in a workplace. It is based on a mathematical approach using a Jacobian matrix. In addition, using the presented method, identification of other kinematic parameters of a robot is possible. The Universal Robot UR3 was later chosen to prove the working principle in both simulations and experiment, with a simple repeatable low-cost solution for such a task—image analysis to detect tag markers. The results showing the accuracy of the system are included and discussed.

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An automatic calibration approach for a multi-camera-robot system

Cited by 7 publications

References 7 publications

Online Multi Camera-IMU Calibration

Online Multi Camera-IMU Calibration

METRIC—Multi-Eye to Robot Indoor Calibration Dataset

Camera-Based Method for Identification of the Layout of a Robotic Workcell

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