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Institute
This paper presents a novel algorithm for computing absolute space representations (ASRs) in Yeap, W.K. and Jefferies, M. (1988) for mobile robots equipped with sonar sensors and an odometer. The robot is allowed to wander freely (i.e. without following any fixed path) along the corridors in an office environment from a given start point to an end point. It then wanders from the end point back to the start point. The resulting ASRs computed in both directions are shown
This paper shows how a mobile robot equipped with sonar sensors and an odometer is used to test ideas about cognitive mapping. The robot first explores an office environment and computes a "cognitive map" which is a network of ASRs [1]. The robot generates two networks, one for the outward journey and the other for the journey home.
It is shown that both networks are different. The two networks, however, are not merged to form a single network. Instead, the robot attempts to use distance information implicit in the shape of each ASR to find its way home. At random positions in the homeward journey, the robot calculates its orientation towards home. The robot's performances for both problems are evaluated and found to be surprisingly accurate.
The paper presents an extended hand-eye calibration approach that, in contrast to the standard method, does not require a calibration pattern for determining camera position and orientation. Instead, a structure-from-motion algorithm is applied for obtaining the eye-data that is necessary for computing the unknown hand-eye transformation.
Different ways of extending the standard algorithm are presented, which mainly involves the estimation of a scale factor in addition to rotation and translation. The proposed methods are experimentally compared using data obtained from an optical tracking system that determines the pose of an endoscopic camera.
The approach is of special interest in our clinical setup, as the usage of an unsterile calibration pattern is difficult in a sterile environment.
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.
OBJECTIVES:
To generate a fast and robust 3-D visualization of the operation site during minimal invasive surgery.
METHODS:
Light fields are used to model and visualize the 3-D operation site during minimal invasive surgery. An endoscope positioning robot provides the position and orientation of the endoscope. The a priori un-known transformation from the endoscope plug to the endoscope tip (hand-eye transformation) can either be determined by a three-step algorithm, which includes measuring the endoscope length by hand or by using an automatic hand-eye calibration algorithm. Both methods are described in this paper and their respective computation times and accuracies are compared.
RESULTS:
Light fields were generated during real operations and in the laboratory. The comparison of the two methods to determine the unknown hand-eye transformation was done in the laboratory. The results which are being presented in this paper are: rendered images from the generated light fields, the calculated extrinsic camera parameters and their accuracies with respect to the applied hand-eye calibration method, and computation times.
CONCLUSION:
Using an endoscope positioning robot and knowing the hand-eye transformation, the fast and robust generation of light fields for minimal invasive surgery is possible.
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
Forschungsbericht 2013
(2014)