%0 Conference Paper %1 IAS2004 %A Nüchter, A. %A Surmann, H. %A Hertzberg, J. %B Proceedings of the 8th Conference on Intelligent Autonomous Systems (IAS '04) %C Amsterdam, The Netherlands %D 2004 %K imported %P 963--970 %T Automatic Classification of Objects in 3D Laser Range Scans %U https://robotik.informatik.uni-wuerzburg.de/telematics/download/mrnv2004.pdf %X 3D models of the skin surface of patients are created by ultra-fast holography and automatic scan matching of synchronously recorded holograms. By recording with a pulsed laser and continuous-wave optical reconstruction of the holographic real image, motion artifacts are eliminated. Focal analys is of the real image yields a surface relief of the patient. To generate a complete 360 patient model, several synchronously recorded reliefs are registered by automatic scan matching. We find the transformation consisting of a rotation and a translation that minimizes a cost function containing the Euclidian distances between points pairs from two surface relief maps. A variant of the ICP (Iterative Closest Points) algorithm2 is used to compute such a minimum. We propose a new fast approximation based on kDtrees for the problem of creating the closest point pairs on which the ICP algorithm spends most of its time.

@inproceedings{IAS2004, abstract = {3D models of the skin surface of patients are created by ultra-fast holography and automatic scan matching of synchronously recorded holograms. By recording with a pulsed laser and continuous-wave optical reconstruction of the holographic real image, motion artifacts are eliminated. Focal analys is of the real image yields a surface relief of the patient. To generate a complete 360 patient model, several synchronously recorded reliefs are registered by automatic scan matching. We find the transformation consisting of a rotation and a translation that minimizes a cost function containing the Euclidian distances between points pairs from two surface relief maps. A variant of the ICP (Iterative Closest Points) algorithm2 is used to compute such a minimum. We propose a new fast approximation based on kDtrees for the problem of creating the closest point pairs on which the ICP algorithm spends most of its time. }, added-at = {2017-09-19T13:40:53.000+0200}, address = {Amsterdam, The Netherlands}, author = {N{\"u}chter, A. and Surmann, H. and Hertzberg, J.}, biburl = {https://www.bibsonomy.org/bibtex/2df5b7e16de0329aeadcf14ad05f0b96c/nuechter76}, booktitle = {Proceedings of the 8th Conference on Intelligent Autonomous Systems (IAS '04)}, interhash = {fad2c866497cae9523d2342688a62b33}, intrahash = {df5b7e16de0329aeadcf14ad05f0b96c}, keywords = {imported}, month = {March}, pages = {963--970}, timestamp = {2017-09-29T16:01:21.000+0200}, title = {{A}utomatic {C}lassification of {O}bjects in {3D} {L}aser {R}ange {S}cans}, url = {https://robotik.informatik.uni-wuerzburg.de/telematics/download/mrnv2004.pdf}, year = 2004 }