Scalable and flexible vision-based multi-robot tracking system

Tanoto, Andry; Li, Hanyi; Rückert, Ulrich; Sitte, Joaquin

doi:10.1109/isic.2012.6398261

Cited by 6 publications

(6 citation statements)

References 5 publications

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“…When compared to markerless methods, e.g., [4], the accuracy is over an order of magnitude better. Moreover, unlike the solutions presented in [13] and [12], it facilitates full pose recovery. The reliable observation of the robot-mounted marker may however be impossible or unfeasible in vast spaces – in such a case the calibration and tracking solutions based on natural features like [14] or [15] are a better choice, provided that there are enough natural features available for tracking.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…In [12] and [13], the authors present multi-camera systems capable of tracking multiple robots. The systems use no or minimum artificial markers for the identification of the tracked objects and the tracking process allows only for the reconstruction of the paths of the robots with limited information on orientation.…”

Section: Introductionmentioning

confidence: 99%

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Calibration of the Multi-camera Registration System for Visual Navigation Benchmarking

Schmidt

Kasiński

Kraft

et al. 2014

International Journal of Advanced Robotic Systems

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This paper presents the complete calibration procedure of a multi-camera system for mobile robot motion registration. Optimization-based, purely visual methods for the estimation of the relative poses of the motion registration system cameras, as well as the relative poses of the cameras and markers placed on the mobile robot were proposed. The introduced methods were applied to the calibration of the system and the quality of the obtained results was evaluated. The obtained results compare favourably with the state of the art solutions, allowing the use of the considered motion registration system for the accurate reconstruction of the mobile robot trajectory and to register new datasets suitable for the benchmarking of indoor, visual-based navigation algorithms.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Calibration of the Multi-camera Registration System for Visual Navigation Benchmarking

Schmidt

Kasiński

Kraft

et al. 2014

International Journal of Advanced Robotic Systems

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“…The downside of this system is, that it does not handle the camera's extrinsic calibration explicitly, nor the fusion of detected markers in the cameras' frustum-intersections. Teleworkbench [7] tracks and identifies multiple fiducial markers in SO(2) in a single camera setup. The Experimental Testbed for Large Multirobot Teams [8] uses LED based markers that flash with their corresponding ID, which is rectified within SO(2) by multiple cameras.…”

Section: Related Workmentioning

confidence: 99%

“…If range measurements already exists and intrinsic parameters are sufficiently calibrated, graph SLAM Fig. 1: Tracking and benchmark systems [2], [3], [5], [6], [7], [8], [9] (from left to right and top to bottom)…”

Section: Marker Based Calibrationmentioning

confidence: 99%

Fiducial Marker based Extrinsic Camera Calibration for a Robot Benchmarking Platform

Korthals

Wolf

Rudolph

et al. 2019

2019 European Conference on Mobile Robots (ECMR)

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Evaluation of robotic experiments requires physical robots as well as position sensing systems. Accurate systems detecting sufficiently all necessary degrees of freedom, like the famous Vicon system, are commonly too expensive. Therefore, we target an economical multi-camera based solution by following these three requirements: Using multiple cameras to track even large laboratory areas, applying fiducial marker trackers for pose identification, and fuse tracking hypothesis resulting from multiple cameras via extended Kalman filter (i.e. ROS's robot localization). While the registration of a multi-camera system for collaborative tracking remains a challenging issue, the contribution of this paper is as follows: We introduce the framework of Cognitive Interaction Tracking (CITrack). Then, common fiducial marker tracking systems (ARToolKit, April-Tag, ArUco) are compared with respect to their maintainability. Lastly, a graph-based camera registration approach in SE(3), using the fiducial marker tracking in a multi-camera setup, is presented and evaluated.

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