{"id":3374,"date":"2026-01-08T07:37:20","date_gmt":"2026-01-08T07:37:20","guid":{"rendered":"https:\/\/www.satlabgeo.com\/?post_type=industries&p=3374"},"modified":"2026-01-08T07:37:20","modified_gmt":"2026-01-08T07:37:20","slug":"complex-building-survey-in-a-forested-area-using-sl9-slam-rtk","status":"publish","type":"industries","link":"https:\/\/www.satlabgeo.com\/in\/industries\/complex-building-survey-in-a-forested-area-using-sl9-slam-rtk\/","title":{"rendered":"Complex Building Survey in a Forested Area Using SL9 SLAM RTK"},"content":{"rendered":"
Download full article >>><\/a><\/div>\n

Project Background<\/strong><\/em><\/span>
\nThis project involved surveying and as-built documentation of a resort property in Bali. The resort is located within a densely wooded area, and the survey focused on a building that had been constructed but not yet finished. The client required high-quality point cloud data suitable for as-built verification and indoor modeling, providing a reliable basis for subsequent design, interior fit-out, or renovation planning. In addition, the project demanded the integration of indoor and outdoor coordinates through RTK or control points to ensure a consistent and accurate spatial reference.
\n<\/span><\/p>\n

\"Lidar<\/p>\n

Challenges<\/strong><\/em><\/span>
\nThe dense vegetation and steep terrain severely obstructed GNSS signals and limited line-of-sight, making traditional RTK surveying unreliable. Although total stations offer high accuracy, the complex environment and large slopes made instrument setup, relocation, and measurement inconvenient and time-consuming, resulting in low efficiency that could not meet project timelines. Meanwhile, the client\u2019s strict requirements for point cloud quality\u2014accurate wall lines, clear structural representation, and smooth surfaces\u2014posed further challenges that conventional surveying methods struggled to satisfy.<\/span><\/p>\n

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Solution<\/strong><\/em><\/span>
\nTo address the challenges of a complex site environment and high-quality point cloud requirements, the SL9 SLAM RTK system was deployed. By combining SLAM laser scanning with RTK positioning, SL9 efficiently captured continuous, stable spatial data even under dense canopy and limited GNSS conditions. This approach ensured engineering-grade point cloud quality while maintaining operational efficiency, providing a reliable foundation for as-built documentation, indoor modeling, and subsequent design or renovation applications.<\/span>
\nWorkflow<\/strong><\/em><\/span><\/p>\n

1.Quick Deployment and One-Touch Scanning<\/p>\n

The SL9 SLAM RTK features an integrated design, allowing operators to start scanning with a single touch on the screen. Even non-specialist personnel can independently perform the survey after brief training, reducing labor and training costs.<\/span><\/p>\n

2. RTK-Enabled, Scan-to-Map Workflow<\/p>\n

The built-in RTK system enables operators to scan areas of interest without following strict paths, offering flexibility and efficiency in complex building environments.<\/span><\/p>\n

3. Unified Indoor-Outdoor Coordinate System<\/p>\n

Equipped with a high-precision RTK module, SL9 ensures indoor and outdoor data are aligned within a single spatial reference, facilitating seamless integration for design, construction, and management purposes<\/span>
\nResults<\/strong><\/em><\/span><\/p>\n

1. High-Quality Point Clouds<\/p>\n

The resulting point clouds clearly represent walls, columns, and structural elements with smooth surfaces and minimal noise, accurately reflecting the as-built condition of the building.<\/span><\/p>\n

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2. Fast Measurement and Quantification<\/p>\n

From the point cloud, operators can quickly calculate building perimeter, floor area, and volume, facilitating property surveying, construction verification, and project management while minimizing repeat site visits.<\/span><\/p>\n

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3. Point Cloud Slicing and CAD Integration<\/p>\n

Data can be imported into SAT-LiDAR software for slicing, extracting precise wall lines and structural boundaries. The vectorized output can be seamlessly imported into CAD for drafting, measurement, and engineering analysis, completing a fully integrated workflow from on-site acquisition to project documentation.<\/span><\/p>\n

\"Lidar<\/p>\n

\"Lidar<\/p>","protected":false},"excerpt":{"rendered":"

This case showcases the SL9 SLAM RTK, capturing high-quality point clouds of a Bali resort under dense vegetation and limited GNSS conditions, providing accurate as-built data for design and renovation.<\/p>","protected":false},"featured_media":0,"parent":0,"template":"","meta":{"_links_to":"","_links_to_target":""},"yoast_head":"\nComplex Building Survey in a Forested Area Using SL9 SLAM RTK | Global Satellite Positioning Solutions | SatLab Geosolutions AB<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.satlabgeo.com\/in\/industries\/complex-building-survey-in-a-forested-area-using-sl9-slam-rtk\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Complex Building Survey in a Forested Area Using SL9 SLAM RTK | Global Satellite Positioning Solutions | SatLab Geosolutions AB\" \/>\n<meta property=\"og:description\" content=\"This case showcases the SL9 SLAM RTK, capturing high-quality point clouds of a Bali resort under dense vegetation and limited GNSS conditions, providing accurate as-built data for design and renovation.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.satlabgeo.com\/in\/industries\/complex-building-survey-in-a-forested-area-using-sl9-slam-rtk\/\" \/>\n<meta property=\"og:site_name\" content=\"Global Satellite Positioning Solutions | SatLab Geosolutions AB\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.satlabgeo.com\/wp-content\/uploads\/2026\/01\/SL9-SLAM-RTK-_Complex-Building-Survey-.webp\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" 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