Robust hand-eye self-calibration

Ruland, Thomas; Pajdla, Tomáš; Krüger, Lars

doi:10.1109/itsc.2011.6083013

Cited by 5 publications

(5 citation statements)

References 21 publications

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“…Calibration performance is also either equal or superior to current state of the art correspondence based hand-eye calibration methods. The rotational accuracy is in the same order of magnitude or even better when compared to the results published by Seo et al [13] and Ruland et al [11] respectively. The translational accuracy is improved over the contribution of Heller et al [7].…”

Section: Evaluation On Benchmark Datasetsupporting

confidence: 72%

“…throughout the expected noise levels. The effect of improving estimations at increasing noise levels (between 0 and 1 × 10 −3 rad) was analyzed by Ruland et al in [11]. The average computation time in these simulated experiments was approximately 107 s.…”

Section: Sensitivity To Noise Affected Interest Pointsmentioning

confidence: 99%

“…Since the described method does not contain an algorithmic element of correspondence selection, outliers had to be rejected manually. The selection procedures presented in [11] fit very well into the presented optimization principle, but could not be integrated into this paper due to space constraints.…”

Section: Evaluation On Benchmark Datasetmentioning

confidence: 99%

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Globally optimal hand-eye calibration

Ruland

Pajdla

Krüger

2012

2012 IEEE Conference on Computer Vision and Pattern Recognition

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This paper introduces simultaneous globally optimal hand-eye self-calibration in both its rotational and translational components. The main contributions are new feasibility tests to integrate the hand-eye calibration problem into a branch-and-bound parameter space search. The presented method constitutes the first guaranteed globally optimal estimator for simultaneous optimization of both components with respect to a cost function based on reprojection errors. The system is evaluated in both synthetic and real world scenarios. The employed benchmark dataset is published online 1 to create a common point of reference for evaluation of hand-eye self-calibration algorithms.

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Section: Evaluation On Benchmark Datasetsupporting

confidence: 72%

Section: Sensitivity To Noise Affected Interest Pointsmentioning

confidence: 99%

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Globally optimal hand-eye calibration

Ruland

Pajdla

Krüger

2012

2012 IEEE Conference on Computer Vision and Pattern Recognition

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“…Each set consists of full images which are used as input. Besides constraining hypotheses to be on the ground plane known from camera calibration [16], [5], no search space limitation/selection is performed. To classify detections into true and false positives, the standard PASCAL VOC coverage metric [17] is thresholded at a minimum coverage of 50 %.…”

Section: Detection Performancementioning

confidence: 99%

Feature selection for automotive object detection tasks - A study

Gabb

Wagner

Gressmann

et al. 2012

2012 IEEE Second International Conference on Consumer Electronics - Berlin (ICCE-Berlin)

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For object detection in monocular images, the Boosted Cascade [1] has become the standard approach for driver assistance systems. This paper studies the discriminative power of different features for common automotive object detection tasks: pedestrian and vehicle detection using infrared cameras at night, as well as pedestrian and vehicle detection in daylight conditions. It is shown that the use of intra-stage information propagation with Activation History Features (AHFs) [2] significantly speeds up the detection at the same detection rate. Thus, AHFs offer a speedup at no cost.

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“…Strobl and Hirzinger proposed a new adaptive error model that helped improve the solution to AX = XB and AX = ZB [17]. Ruland proposed a self-calibration method that took projection error as its cost function and optimized it using branch-and-bound [18].…”

Section: Introductionmentioning

confidence: 99%

Robust and Accurate Hand–Eye Calibration Method Based on Schur Matric Decomposition

Wang

2019

Sensors

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To improve the accuracy and robustness of hand–eye calibration, a hand–eye calibration method based on Schur matric decomposition is proposed in this paper. The accuracy of these methods strongly depends on the quality of observation data. Therefore, preprocessing observation data is essential. As with traditional two-step hand–eye calibration methods, we first solve the rotation parameters and then the translation vector can be immediately determined. A general solution was obtained from one observation through Schur matric decomposition and then the degrees of freedom were decreased from three to two. Observation data preprocessing is one of the basic unresolved problems with hand–eye calibration methods. A discriminant equation to delete outliers was deduced based on Schur matric decomposition. Finally, the basic problem of observation data preprocessing was solved using outlier detection, which significantly improved robustness. The proposed method was validated by both simulations and experiments. The results show that the prediction error of rotation and translation was 0.06 arcmin and 1.01 mm respectively, and the proposed method performed much better in outlier detection. A minimal configuration for the unique solution was proven from a new perspective.

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Robust hand-eye self-calibration

Cited by 5 publications

References 21 publications

Globally optimal hand-eye calibration

Globally optimal hand-eye calibration

Feature selection for automotive object detection tasks - A study

Robust and Accurate Hand–Eye Calibration Method Based on Schur Matric Decomposition

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