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Wepresentaniterativeregistrationalgorithmfor aligning two differently scaled 3-D point sets. It extends the popular Iterative Closest Point (ICP) algorithm by estimating a scale factor between the two point sets in every iteration.
The presented algorithm is especially useful for the registration of point sets generated by structure-frommotion algorithms, which only reconstruct the 3-D structure of a scene upto scale. LiketheoriginalICPalgorithm,thepresentedalgorithm requires a rough pre-alignment of the point sets.
In order to determine the necessary accuracy of the pre-alignment, wehaveexperimentallyevaluatedthebasinofconvergence of the algorithm with respect to the initial rotation, translation, andscale factor between the two point sets.
The paper presents a new vectorquantization based Approach for selecting well-suited data for hand-eye calibration from a given sequence of hand and eye movements.
Data selection is essential if control of the movements used for calibration is not possible, especially when using continuously recorded data. The new algorithm is compared to another method for data selection as well as to the processing of subsequent movements.
Experimental results on real and synthetic data sets show the superior performance of the new approach with respect to calibration errors and computation time.
Real data has been obtained from an optical tracking system and a camera mounted on an endoscope, the goal being the reconstruction of medical lightfields.
This paper presents an approach for applying a dual quaternion hand–eye calibration algorithm on an endoscopic surgery robot. Special focus is on robustness, since the error of position and orientation data provided by the robot can be large depending on the movement actually executed.
Another inherent problem to all hand–eye calibration methods is that non–parallel rotation axes must be used; otherwise, the calibration will fail.
Thus we propose a method for increasing the numerical stability by selecting an optimal set of relative movements from the recorded sequence.
Experimental evaluation shows the error in the estimated transformation when using well–suited and ill–suited data. Additionally, we show how a RANSAC approach can be used for eliminating the erroneous robot data from the selected movements.
Robust registration of two 3-D point sets is a common problem in computer vision.
The iterative closest point (ICP) algorithm is undoubtedly the most popular algorithm for solving this kind of problem. In this paper, we present the Picky ICP algorithm, which has been created by merging several extensions of the standard ICP algorithm, thus improving its robustness and computation time.
Using pure 3-D point sets as input data, we do not consider additional information like point color or neighborhood relations. In addition to the standard ICP algorithm and the Picky ICP algorithm proposed in this paper, a robust algorithm due to Masuda and Yokoya and the RICP algorithm by Trucco et al. are evaluated.
We have experimentally determined the basin of convergence, robustness to noise and outliers, and computation time of these four ICP based algorithms