{"id":1345,"date":"2022-07-28T06:00:40","date_gmt":"2022-07-28T06:00:40","guid":{"rendered":"https:\/\/webroad-seisaku.com\/fujiko-test\/?p=1345"},"modified":"2023-08-07T01:21:09","modified_gmt":"2023-08-07T01:21:09","slug":"kudan-3d-lidar-slam-accelerated-on-nvidia-jetson-platform","status":"publish","type":"post","link":"https:\/\/webroad-seisaku.com\/fujiko-test\/archives\/1345","title":{"rendered":"Kudan releases its latest update on 3D-Lidar SLAM powered by the NVIDIA Jetson platform with up to 120% acceleration result"},"content":{"rendered":"<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-1346 size-full\" src=\"https:\/\/webroad-seisaku.com\/fujiko-test\/wp-content\/uploads\/2022\/07\/NVIDIA_pic1.png\" alt=\"\" width=\"885\" height=\"335\" srcset=\"https:\/\/webroad-seisaku.com\/fujiko-test\/wp-content\/uploads\/2022\/07\/NVIDIA_pic1.png 885w, https:\/\/webroad-seisaku.com\/fujiko-test\/wp-content\/uploads\/2022\/07\/NVIDIA_pic1-300x114.png 300w, https:\/\/webroad-seisaku.com\/fujiko-test\/wp-content\/uploads\/2022\/07\/NVIDIA_pic1-768x291.png 768w\" sizes=\"auto, (max-width: 885px) 100vw, 885px\" \/><\/p>\n<p>Kudan Inc. (headquartered in Shibuya-ku, Tokyo; CEO Daiu Ko, hereafter \u201cKudan\u201d) is pleased to announce that Kudan is releasing a new version of 3D-Lidar SLAM that is optimized for the NVIDIA Jetson edge AI and robotics platform.<\/p>\n<p>Kudan is a Preferred Partner in the NVIDIA Partner Network, working on autonomous mobile robot (AMR) applications with both Visual SLAM and 3D-Lidar SLAM. While a number of companies are developing their autonomous mobile machines using Kudan\u2019s SLAM software and the NVIDIA Jetson platform, this update is designed to help customers take full advantage of the Jetson platform\u2019s hardware capabilities, and Kudan\u2019s advanced lidar SLAM software.<\/p>\n<p>Kudan\u2019s 3D-Lidar SLAM accelerated on NVIDIA Jetson brings significant value to AMR applications by freeing up processing capacity for other resource-intensive tasks and increasing overall stability. Thanks to the continuing 3D-Lidar price reductions and overall performance improvements, more and more robotics OEMs are evaluating 3D-Lidar SLAM for their localization requirements. However, given that some of the sensors generate more than 1 million points per second, processing SLAM with this amount of data in real-time can be a challenge. For academic and research purposes, it is possible to use a high-power computer to process 3D-Lidar SLAM but the cost can be prohibitive for large-scale production and adoption.<\/p>\n<p>Kudan leveraged NVIDIA\u2019s math functions optimized for its CUDA architecture in the 3D-Lidar SLAM\u2019s pose estimation module to process data more efficiently on the GPU. This not only accelerates the overall processing time but also offloads a considerable portion of the processing from the CPU. Kudan tested its performance on the NVIDIA Jetson AGX Xavier module using KITTI datasets, one of the most well-known open datasets for testing 3D-Lidar SLAM, and observed 120% acceleration compared to using only CPUs and 80% acceleration on a desktop system.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-1347 size-full\" src=\"https:\/\/webroad-seisaku.com\/fujiko-test\/wp-content\/uploads\/2022\/07\/NVIDIA_pic2.png\" alt=\"\" width=\"885\" height=\"318\" srcset=\"https:\/\/webroad-seisaku.com\/fujiko-test\/wp-content\/uploads\/2022\/07\/NVIDIA_pic2.png 885w, https:\/\/webroad-seisaku.com\/fujiko-test\/wp-content\/uploads\/2022\/07\/NVIDIA_pic2-300x108.png 300w, https:\/\/webroad-seisaku.com\/fujiko-test\/wp-content\/uploads\/2022\/07\/NVIDIA_pic2-768x276.png 768w\" sizes=\"auto, (max-width: 885px) 100vw, 885px\" \/><\/p>\n<p>Kudan sees more acceleration opportunities for 3D-Lidar SLAM and also plans to implement similar acceleration for Visual SLAM on NVIDIA CUDA architecture. This series of efforts will benefit many robotics OEMs and users. Also, creating digital twins and accurate 3D point clouds in geospatial and general mapping applications are increasingly in high demand, where NVIDIA GPU-accelerated computing and Kudan 3D-Lidar SLAM shine together through NVIDIA\u2019s processing capability and Kudan\u2019s SLAM performance.<\/p>\n<p>Please access <a href=\"https:\/\/webroad-seisaku.com\/fujiko-test\/blog\/kudan-joins-nvidia-partner-network-to-expand-slam-opportunities\/\" target=\"_blank\" rel=\"noopener\">this link<\/a> for our previous release about Kudan\u2019s relationship with NVIDIA.<\/p>\n<p><strong>About Kudan Inc.<\/strong><br \/>\nKudan (Tokyo Stock Exchange securities code: 4425) is a deep tech research and development company specializing in algorithms for artificial perception (AP). As a complement to artificial intelligence (AI), AP functions allow machines to develop autonomy. Currently, Kudan is using its high-level technical innovation to explore business areas based on its own milestone models established for deep tech which provide wide-ranging impact on several major industrial fields.<br \/>\nFor more information, please refer to Kudan\u2019s website at <a href=\"https:\/\/webroad-seisaku.com\/fujiko-test\/\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/webroad-seisaku.com\/fujiko-test\/<\/a>.<\/p>\n<p>\u25a0Company Details<br \/>\nName: Kudan Inc.<br \/>\nSecurities Code: 4425<br \/>\nRepresentative: CEO Daiu Ko<\/p>\n<p>\u25a0For more details, please contact us from <a href=\"https:\/\/webroad-seisaku.com\/fujiko-test\/contact\" target=\"_blank\" rel=\"noopener noreferrer\">here<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Kudan Inc. (headquartered in Shibuya-ku, Tokyo; CEO Daiu Ko, hereafter \u201cKudan\u201d) is pleased to announce that Kudan is releasing a new version of 3D-Lidar SLAM that is optimized for the NVIDIA Jetson edge AI and robotics platform. Kudan is a Preferred Partner in the NVIDIA Partner Network, working on autonomous mobile robot (AMR) applications with [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1350,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"sns_share_botton_hide":"","vkExUnit_sns_title":"","_vk_print_noindex":"","sitemap_hide":"","_veu_custom_css":"","veu_display_promotion_alert":"","vkexunit_cta_each_option":"","_lightning_design_setting":[],"header_top_description":"","footnotes":""},"categories":[1],"tags":[62,248,249,5,61,113,247,54],"class_list":["post-1345","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-press","tag-3d-lidar-slam","tag-acceleration","tag-gpu","tag-kudan","tag-kudan-3d-lidar-slam","tag-nvidia","tag-nvidia-jetson-platform","tag-nvidia-partner-network"],"veu_head_title_object":{"title":"","add_site_title":""},"_links":{"self":[{"href":"https:\/\/webroad-seisaku.com\/fujiko-test\/wp-json\/wp\/v2\/posts\/1345","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/webroad-seisaku.com\/fujiko-test\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/webroad-seisaku.com\/fujiko-test\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/webroad-seisaku.com\/fujiko-test\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/webroad-seisaku.com\/fujiko-test\/wp-json\/wp\/v2\/comments?post=1345"}],"version-history":[{"count":1,"href":"https:\/\/webroad-seisaku.com\/fujiko-test\/wp-json\/wp\/v2\/posts\/1345\/revisions"}],"predecessor-version":[{"id":1348,"href":"https:\/\/webroad-seisaku.com\/fujiko-test\/wp-json\/wp\/v2\/posts\/1345\/revisions\/1348"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/webroad-seisaku.com\/fujiko-test\/wp-json\/wp\/v2\/media\/1350"}],"wp:attachment":[{"href":"https:\/\/webroad-seisaku.com\/fujiko-test\/wp-json\/wp\/v2\/media?parent=1345"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/webroad-seisaku.com\/fujiko-test\/wp-json\/wp\/v2\/categories?post=1345"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/webroad-seisaku.com\/fujiko-test\/wp-json\/wp\/v2\/tags?post=1345"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}