Elements of information theory
Elements of information theory
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Object modelling by registration of multiple range images
Image and Vision Computing - Special issue: range image understanding
Estimating 3-D rigid body transformations: a comparison of four major algorithms
Machine Vision and Applications - Special issue on performance evaluation
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Geometry of Algorithms with Orthogonality Constraints
SIAM Journal on Matrix Analysis and Applications
Multi-scale EM-ICP: A Fast and Robust Approach for Surface Registration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
The Softassign Procrustes Matching Algorithm
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Robust Algorithm for Point Set Registration Using Mixture of Gaussians
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Geometry and Convergence Analysis of Algorithms for Registration of 3D Shapes
International Journal of Computer Vision
SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
Machine Vision and Applications - Integrated Imaging and Vision Techniques for Industrial Inspection
Model-Based Multiple Rigid Object Detection and Registration in Unstructured Range Data
International Journal of Computer Vision
Motion planning efficient trajectories for industrial bin-picking
International Journal of Robotics Research
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3D vision-guided manipulation of components is a key problem of industrial machine vision. In this paper, we focus on the localization and pose estimation of known industrial objects from 3D measurements delivered by a scanning sensor. Since local information extracted from these measurements is unreliable due to noise, spatially unstructured measurements and missing detections, we present a novel objective function for robust registration without using correspondence information, based on the likelihood of model points. Furthermore, by extending Runge–Kutta-type integration directly to the group of Euclidean transformation, we infer object pose by computing the gradient flow directly on the related manifold. Comparison of our approach to existing state of the art methods shows that our method is more robust against poor initializations while having comparable run-time performance.