A Frequency Domain Technique for Range Data Registration
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Technique for the Extraction and Tracking of Surfaces in Range Image Sequences
Revised Papers from the International Workshop on Sensor Based Intelligent Robots
Fast ICP Algorithms for Shape Registration
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Multi-Feature Matching Algorithm for Free-Form 3D Surface Registration
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Robust surface matching for registration
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
Self-calibration of a light striping system by matching multiple 3-D profile maps
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
Extraction and tracking of surfaces in range image sequences
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
Verification of engineering models based on bipartite graph matching for inspection applications
GMP'06 Proceedings of the 4th international conference on Geometric Modeling and Processing
Comparing ICP variants on real-world data sets
Autonomous Robots
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This paper considers the matching of 3D objects by a geometric approach based on the iterative closest point algorithm (ICP), which, starting from an initial configuration of two rigid objects, iteratively finds their best correspondence. The algorithm does not converge always to the best solution. It can be trapped in a local minimum and miss the optimum matching. While the convergence of this algorithm towards the global minimum is known to depend largely on the initial configuration of test and model objects, this paper investigates the quantitative nature of this dependence. Considering the space C of relative configurations of the two objects to be compared, we call range of successful initial configurations, or SIC-range, the subspace of C which configurations bring the algorithm to converge to the global minimum. In this paper, we present a frame for analyzing the SIC-range of 3D objects and present a number of original experimental results assessing the SIC-range of a number of real 3D objects.