{"id":6950,"date":"2012-01-17T16:29:19","date_gmt":"2012-01-17T14:29:19","guid":{"rendered":"http:\/\/hgpu.org\/?p=6950"},"modified":"2012-01-17T16:29:19","modified_gmt":"2012-01-17T14:29:19","slug":"data-registration-module-a-component-of-semantic-simulation-engine","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6950","title":{"rendered":"Data registration module &#8211; a component of semantic simulation engine"},"content":{"rendered":"<p>In this paper the data registration module being a component of semantic simulation engine is shown. An improved implementation of ICP (Iterative Closest Point) algorithm based on GPGPU (General-purpose computing on graphics processing units) is proposed. The main achievement is on-line aliment of two data sets composed of up to 262144 3D points, therefore it can be used during robot motion. The algorithm uses GPGPU NVIDIA CUDA for NNS (Nearest Neighborhood Search) computation and to improve the performance all ICP steps are implemented also on GPU. Experiments performed in INDOOR and OUDROOR environments show benefits of parallel computation applied for on-line 3D map building. Empirical validation using new generation CUDA architecture, named Fermi is shown.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper the data registration module being a component of semantic simulation engine is shown. An improved implementation of ICP (Iterative Closest Point) algorithm based on GPGPU (General-purpose computing on graphics processing units) is proposed. The main achievement is on-line aliment of two data sets composed of up to 262144 3D points, therefore it [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,11,89,3],"tags":[1787,1782,14,94,349,20,974],"class_list":["post-6950","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-computer-science","tag-cuda","tag-data-structures-and-algorithms","tag-nearest-neighbour","tag-nvidia","tag-nvidia-geforce-gtx-580"],"views":2114,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6950","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=6950"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6950\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6950"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6950"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6950"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}