{"id":5260,"date":"2026-07-28T18:51:41","date_gmt":"2026-07-28T09:51:41","guid":{"rendered":"https:\/\/smilesoftware.org\/?p=5260"},"modified":"2026-07-29T12:35:45","modified_gmt":"2026-07-29T03:35:45","slug":"gps%e3%81%8c%e4%bd%bf%e3%81%88%e3%81%aa%e3%81%84%e7%92%b0%e5%a2%83%e3%81%a73d%e3%83%9e%e3%83%83%e3%83%94%e3%83%b3%e3%82%b0%e3%82%92%e5%ae%9f%e7%8f%be%e3%81%99%e3%82%8blidar%ef%bc%8fslam%e3%82%b7","status":"publish","type":"post","link":"https:\/\/smilesoftware.org\/en\/gps%E3%81%8C%E4%BD%BF%E3%81%88%E3%81%AA%E3%81%84%E7%92%B0%E5%A2%83%E3%81%A73d%E3%83%9E%E3%83%83%E3%83%94%E3%83%B3%E3%82%B0%E3%82%92%E5%AE%9F%E7%8F%BE%E3%81%99%E3%82%8Blidarslam%E3%82%B7\/","title":{"rendered":"Development of a LiDAR\/SLAM System for 3D Mapping in GPS-Denied Environments"},"content":{"rendered":"<div class=\"wp-block-group smile-post-content\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><header class=\"smile-article-header\"><div><span class=\"smile-category-pill\">Project<\/span><span class=\"smile-date\"><\/span><\/div><h1 class=\"smile-article-title\">Development of a LiDAR\/SLAM System for 3D Mapping in GPS-Denied Environments<\/h1><p class=\"smile-excerpt\">At construction, infrastructure, and manufacturing sites, teams often need to assess conditions while understanding the positional relationships between equipment and work routes, even in environments where GPS signals do not reach, such as tunnels and indoor facilities. In this case, a system was built that combines LiDAR, IMU, and cameras to estimate relative positions and generate 3D maps using SLAM.<\/p><\/header>\n\n\n<ul style=\"color:#475569;padding-top:var(--wp--preset--spacing--30);padding-bottom:var(--wp--preset--spacing--30);font-size:17px\" class=\"wp-block-list has-text-color has-link-color wp-elements-0b8b8bb7b30c8b53e01380266290f045\">\n<li><strong>Target Area:<\/strong> 3D mapping in tunnels and indoor environments where GPS is unavailable<\/li>\n\n\n\n<li><strong>SMILE\u2019s Role:<\/strong> Sensor integration, ROS2-based processing, point cloud generation, Web visualization, validation, and tuning<\/li>\n\n\n\n<li><strong>Team Structure:<\/strong> 5 members<\/li>\n\n\n\n<li><strong>Timeline:<\/strong> 15 months<\/li>\n\n\n\n<li><strong>Configuration:<\/strong> two LiDAR units, one IMU\/camera system, Edge PC, and Web viewer<\/li>\n\n\n\n<li><strong>Outcome:<\/strong> Established a workflow that treats mapping and sensor data integration as a continuous process, consolidating verification tasks that previously required switching between multiple tools.<\/li>\n<\/ul>\n\n\n<nav class=\"smile-toc\" aria-label=\"Table of Contents\"><h2 class=\"smile-toc__title\">Table of Contents<\/h2><div class=\"smile-toc__grid\"><a class=\"smile-toc__item\" href=\"#section-1\"><b class=\"smile-toc__number\">01<\/b><span class=\"smile-toc__label\">Customer Challenges<\/span><\/a><a class=\"smile-toc__item\" href=\"#section-2\"><b class=\"smile-toc__number\">02<\/b><span class=\"smile-toc__label\">Project Objectives<\/span><\/a><a class=\"smile-toc__item\" href=\"#section-3\"><b class=\"smile-toc__number\">03<\/b><span class=\"smile-toc__label\">SMILE\u2019s Scope of Support<\/span><\/a><a class=\"smile-toc__item\" href=\"#section-4\"><b class=\"smile-toc__number\">04<\/b><span class=\"smile-toc__label\">Key Development and Improvement Points<\/span><\/a><a class=\"smile-toc__item\" href=\"#section-5\"><b class=\"smile-toc__number\">05<\/b><span class=\"smile-toc__label\">Technologies Used<\/span><\/a><a class=\"smile-toc__item\" href=\"#section-6\"><b class=\"smile-toc__number\">06<\/b><span class=\"smile-toc__label\">Technical Challenges and How We Addressed Them<\/span><\/a><a class=\"smile-toc__item\" href=\"#section-7\"><b class=\"smile-toc__number\">07<\/b><span class=\"smile-toc__label\">Implementation Benefits<\/span><\/a><a class=\"smile-toc__item\" href=\"#section-8\"><b class=\"smile-toc__number\">08<\/b><span class=\"smile-toc__label\">Key points for an offshore development structure<\/span><\/a><a class=\"smile-toc__item\" href=\"#section-09\"><b class=\"smile-toc__number\">09<\/b><span class=\"smile-toc__label\">Conclusion<\/span><\/a><\/div><\/nav>\n\n\n<div id=\"section-1\" class=\"wp-block-group smile-section\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><div class=\"smile-section__head\"><div class=\"smile-section__number\">01<\/div><h2 class=\"smile-section__title\">Customer Challenges<\/h2><\/div>\n\n\n<p style=\"font-size:16px\">The customer needed a way to inspect sites while tracking equipment location in environments where GPS is unavailable, such as tunnels and indoor facilities. However, GPS-dependent operations could not provide position estimation in these settings, making it difficult to link measurement data with actual movement on site.<\/p>\n\n\n\n<p style=\"font-size:16px\">When multiple tools are required to check and compare point-cloud data, the validation process becomes complicated, and the burden of continuously evaluating data collected on site increases. The customer needed a configuration that could handle position estimation, map generation, and review as one continuous workflow rather than separate tasks.<\/p>\n<\/div><\/div>\n\n\n\n<div id=\"section-2\" class=\"wp-block-group smile-section\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><div class=\"smile-section__head\"><div class=\"smile-section__number\">02<\/div><h2 class=\"smile-section__title\">Project Objectives<\/h2><\/div>\n\n\n<p style=\"font-size:16px\">The goal of this project was to enable relative positioning and 3D mapping of on-site conditions in environments where GPS is unavailable by using LiDAR together with an IMU and camera.<\/p>\n\n\n\n<p style=\"font-size:16px\">Another priority was to make the collected data easier to use in later processes, creating conditions that would allow point clouds to be reviewed and validated more efficiently. The goal was not simply to connect sensors, but to build a foundation that could support an integrated workflow from on-site measurement through to verification.<\/p>\n<\/div><\/div>\n\n\n\n<div id=\"section-3\" class=\"wp-block-group smile-section\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><div class=\"smile-section__head\"><div class=\"smile-section__number\">03<\/div><h2 class=\"smile-section__title\">SMILE\u2019s Scope of Support<\/h2><\/div>\n\n\n<p style=\"font-size:16px\">SMILE was responsible for the full scope of development, from overall system architecture design to the integration of LiDAR, IMU, and cameras; building the ROS2-based processing platform; point-cloud processing; SLAM integration; map data storage; visualization through a Web viewer; and testing and tuning.<\/p>\n\n\n\n<p style=\"font-size:16px\">Because this was a highly R&amp;D-oriented project, our work went beyond implementation to include designing an architecture that would be easy to validate and improve iteratively. Rather than optimizing sensor input, position estimation, and visualization in isolation, we developed the overall system configuration step by step so that it would function effectively as a whole.<\/p>\n<\/div><\/div>\n\n\n\n<div id=\"section-4\" class=\"wp-block-group smile-section\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><div class=\"smile-section__head\"><div class=\"smile-section__number\">04<\/div><h2 class=\"smile-section__title\">Key Development and Improvement Points<\/h2><\/div>\n\n\n<p style=\"font-size:16px\">In this project, given the constraint that GPS could not be used, we adopted a configuration that improves the stability of position estimation by combining LiDAR with an IMU and camera rather than relying on LiDAR alone. By integrating sensor data and using SLAM to handle self-positioning and the surrounding environment simultaneously, the system is better suited to mapping while in motion rather than one-off measurements.<\/p>\n\n\n\n<p style=\"font-size:16px\">Another important point is that the development was not limited to the algorithm itself; it was designed with ease of review in mind. By using RViz2, an internal review interface, and a Web viewer, SMILE organized processing results in a form that multiple stakeholders could easily understand. This created an environment that supports smoother alignment between technical validation and the envisioned operational use.<\/p>\n<\/div><\/div>\n\n\n\n<div id=\"section-5\" class=\"wp-block-group smile-section\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><div class=\"smile-section__head\"><div class=\"smile-section__number\">05<\/div><h2 class=\"smile-section__title\">Technologies Used<\/h2><\/div>\n\n<div class=\"smile-evidence-panels\"><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--methods\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><circle cx=\"16\" cy=\"32\" r=\"5\"\/><circle cx=\"48\" cy=\"18\" r=\"5\"\/><circle cx=\"48\" cy=\"46\" r=\"5\"\/><path d=\"M21 32h10\"\/><path d=\"M31 32 43 20\"\/><path d=\"M31 32 43 44\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">OS \/ Runtime Environment<\/h3><p>Used both Ubuntu and Windows, configuring runtime environments according to sensor processing and validation requirements.<\/p><\/div><\/article><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--quality\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><path d=\"M16 34 28 46 50 18\"\/><path d=\"M14 14h28\"\/><path d=\"M14 24h18\"\/><path d=\"M14 50h36\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">Middleware \/ Robotics Platform<\/h3><p>Built inter-node communication using ROS2 Humble. Designed processing flows that receive input from LiDAR, IMU, and cameras and connect it to localization and point cloud processing.<\/p><\/div><\/article><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--replace\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><path d=\"M14 18h22v16H14z\"\/><path d=\"M28 30h22v16H28z\"\/><path d=\"m42 16 8 8-8 8\"\/><path d=\"M34 24h16\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">Sensors \/ IoT<\/h3><p>Used Livox LiDAR, IMU, Theta camera, and MQTT. Built a configuration that integrates information from multiple sensors to support the collection and transmission of on-site data.<\/p><\/div><\/article><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--refactor\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><circle cx=\"21\" cy=\"20\" r=\"6\"\/><circle cx=\"43\" cy=\"44\" r=\"6\"\/><path d=\"M27 20h10c7 0 11 4 11 11v7\"\/><path d=\"m42 32 6 6 6-6\"\/><path d=\"M37 44H27c-7 0-11-4-11-11v-7\"\/><path d=\"m22 32-6-6-6 6\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">Position Estimation and Point Cloud Processing<\/h3><p>We used FAST-LIO, SLAM, PCL\/Open3D, and point-cloud registration to estimate relative positions, generate point clouds, and process data for consistency.<\/p><\/div><\/article><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--faq\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><circle cx=\"32\" cy=\"32\" r=\"22\"\/><path d=\"M24 25c1-6 15-7 16 0 1 5-5 7-8 10v4\"\/><path d=\"M32 47h.1\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">Backend Implementation<\/h3><p>Implemented ROS2 nodes in C++ and Python, handling sensor data processing and control logic.<\/p><\/div><\/article><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--climate\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><path d=\"M18 42c-7 0-12-5-12-11s5-11 12-11c3-8 14-11 22-5 4 3 6 7 6 12 6 1 10 5 10 10 0 6-5 10-11 10H18z\"\/><path d=\"M22 50c4 4 10 4 14 0\"\/><path d=\"M40 50c3 3 8 3 11 0\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">Data Storage<\/h3><p>Used PCD and local file storage to support the storage of map data and point cloud data.<\/p><\/div><\/article><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--market\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><path d=\"M10 50h44\"\/><path d=\"M16 42 28 30l8 8 16-20\"\/><path d=\"M40 18h12v12\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">Visualization and Review Environment<\/h3><p>We built an environment for reviewing 3D data and location information using RViz2, an internal review interface, and a Web viewer.<\/p><\/div><\/article><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--operations\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><circle cx=\"32\" cy=\"32\" r=\"9\"\/><path d=\"M32 8v8\"\/><path d=\"M32 48v8\"\/><path d=\"m15 15 6 6\"\/><path d=\"m43 43 6 6\"\/><path d=\"M8 32h8\"\/><path d=\"M48 32h8\"\/><path d=\"m15 49 6-6\"\/><path d=\"m43 21 6-6\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">Other<\/h3><p>Supported TF\/Odom\/Path management and the setup of development environments using Docker.<\/p><\/div><\/article><\/div><\/div><\/div>\n\n\n\n<div id=\"section-6\" class=\"wp-block-group smile-section\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><div class=\"smile-section__head\"><div class=\"smile-section__number\">06<\/div><h2 class=\"smile-section__title\">Technical Challenges and How We Addressed Them<\/h2><\/div>\n\n\n<p style=\"font-size:16px\">The main challenge was to keep SLAM running reliably in an environment where GPS was unavailable, while properly synchronizing LiDAR, IMU, and camera data and layering point clouds with minimal distortion. Because each sensor captures data at different timings and with different characteristics, simply lining up the data can easily lead to unstable position estimation and inconsistent map quality.<\/p>\n\n\n\n<p style=\"font-size:16px\">SMILE combined FAST-LIO with related processing while refining the data-flow design and sensor integration on ROS2, repeatedly validating and tuning the system. It also established a structure that makes results easier to visualize, enabling improvements while checking how point clouds overlap and behave. This created an environment for continuously improving accuracy and stability while maintaining a foundation that supports efficient technical validation.<\/p>\n<\/div><\/div>\n\n\n\n<div id=\"section-7\" class=\"wp-block-group smile-section\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><div class=\"smile-section__head\"><div class=\"smile-section__number\">07<\/div><h2 class=\"smile-section__title\">Implementation Benefits<\/h2><\/div>\n\n\n<p style=\"font-size:16px\">Through this initiative, we established a configuration that supports 3D map generation and relative position verification even in environments where GPS cannot be used. It also reduces reliance on multiple standalone tools for point cloud review, creating a foundation that makes it easier to evaluate mapping results and drive improvement cycles.<\/p>\n<\/div><\/div>\n\n\n\n<div id=\"section-8\" class=\"wp-block-group smile-section\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><div class=\"smile-section__head\"><div class=\"smile-section__number\">08<\/div><h2 class=\"smile-section__title\">Key points for an offshore development structure<\/h2><\/div>\n\n\n<p style=\"font-size:16px\">For this project, we prioritized a structure that allowed the Japan and Vietnam teams to divide responsibilities while advancing development as an R&amp;D initiative built around validation. Requirements and validation points were aligned between the two sides, and the BrSE bridged specification understanding and communication, enabling implementation and verification to proceed while minimizing gaps in understanding.<\/p>\n\n\n\n<p style=\"font-size:16px\">In technical validation projects, it is important to share not only implementation results but also progress along the way. SMILE maintained a continuous cycle of progress reporting, sharing validation results, conducting reviews, and organizing test perspectives, enabling accuracy and stability to be improved step by step. This approach established a development structure that makes it easier to validate and improve solutions, even for highly uncertain themes.<\/p>\n<\/div><\/div>\n\n\n\n<div id=\"section-9\" class=\"wp-block-group smile-section\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><div class=\"smile-section__head\"><div class=\"smile-section__number\">09<\/div><h2 class=\"smile-section__title\">Conclusion<\/h2><\/div>\n\n\n<p style=\"font-size:16px\">3D mapping in environments where GPS is unavailable cannot be achieved effectively by treating sensor integration, position estimation, point cloud processing, and visualization as separate tasks. In this project, SMILE designed and implemented them as an integrated system, building a foundation that could be repeatedly validated and refined toward practical use.<\/p>\n\n\n\n<p style=\"font-size:16px\">For companies in construction, infrastructure, manufacturing, and other sectors that need to work with spatial information in environments where GPS cannot be relied on, this case offers a practical reference for planning how to apply LiDAR and SLAM.<\/p>\n<\/div><\/div>\n\n\n<section class=\"smile-article-cta\"><h2>Looking for a reliable system development partner?<\/h2><p><\/p><ul class=\"smile-list\"><li>At SMILE, we provide end-to-end support tailored to your business challenges and development needs, from requirements analysis and system design to development, testing, operation, and maintenance<\/li><li>We support a wide range of projects, including new system development, enhancement of existing systems, legacy system modernization, AI\/DX initiatives, and system<\/li><li>By combining our Japan-based project coordination with our development team in Vietnam, we deliver flexible support for everything from small-scale enhancements to long-term system development and maintenance<\/li><\/ul><a class=\"smile-article-cta__button\" href=\"\/en\/contact\/\">Discuss Your Project<\/a><\/section><\/div><\/div>","protected":false},"excerpt":{"rendered":"<p>\u304a\u5ba2\u69d8\u306f\u3001\u30c8\u30f3\u30cd\u30eb\u3084\u5c4b\u5185\u8a2d\u5099\u306e\u3088\u3046\u306bGPS\u304c\u5229\u7528\u3067\u304d\u306a\u3044\u74b0\u5883&hellip;&nbsp;<a href=\"https:\/\/smilesoftware.org\/en\/gps%E3%81%8C%E4%BD%BF%E3%81%88%E3%81%AA%E3%81%84%E7%92%B0%E5%A2%83%E3%81%A73d%E3%83%9E%E3%83%83%E3%83%94%E3%83%B3%E3%82%B0%E3%82%92%E5%AE%9F%E7%8F%BE%E3%81%99%E3%82%8Blidarslam%E3%82%B7\/\" class=\"\" rel=\"bookmark\">Read More &raquo;<span class=\"screen-reader-text\">Development of a LiDAR\/SLAM System for 3D Mapping in GPS-Denied Environments<\/span><\/a><\/p>","protected":false},"author":2,"featured_media":5259,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"_themeisle_gutenberg_block_has_review":false,"_ti_tpc_template_sync":false,"_ti_tpc_template_id":"","footnotes":""},"categories":[17],"tags":[47,33,38,45,46],"class_list":["post-5260","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-project","tag-3d","tag-dx","tag-infrastructure-2","tag-iot","tag-lidar-slam"],"acf":[],"rttpg_featured_image_url":{"full":["https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1577-featured-image.png",1672,941,false],"landscape":["https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1577-featured-image.png",1672,941,false],"portraits":["https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1577-featured-image.png",1672,941,false],"thumbnail":["https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1577-featured-image-150x150.png",150,150,true],"medium":["https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1577-featured-image-300x169.png",300,169,true],"large":["https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1577-featured-image-1024x576.png",1024,576,true],"1536x1536":["https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1577-featured-image-1536x864.png",1536,864,true],"2048x2048":["https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1577-featured-image.png",1672,941,false],"trp-custom-language-flag":["https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1577-featured-image-18x10.png",18,10,true],"neve-blog":["https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1577-featured-image-930x620.png",930,620,true]},"rttpg_author":{"display_name":"\u7b20\u539f \u840c","author_link":"https:\/\/smilesoftware.org\/en\/author\/chinh-man\/"},"rttpg_comment":0,"rttpg_category":"<a href=\"https:\/\/smilesoftware.org\/en\/category\/project\/\" rel=\"category tag\">Project<\/a>","rttpg_excerpt":"\u304a\u5ba2\u69d8\u306f\u3001\u30c8\u30f3\u30cd\u30eb\u3084\u5c4b\u5185\u8a2d\u5099\u306e\u3088\u3046\u306bGPS\u304c\u5229\u7528\u3067\u304d\u306a\u3044\u74b0\u5883&hellip;&nbsp;Read More 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