{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T05:45:29Z","timestamp":1767851129999,"version":"3.49.0"},"reference-count":50,"publisher":"SAGE Publications","issue":"4-5","license":[{"start":{"date-parts":[[2014,11,12]],"date-time":"2014-11-12T00:00:00Z","timestamp":1415750400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[2015,4]]},"abstract":"<jats:p> Validating the integrity of pipes is an important task for safe natural gas production and many other operations (e.g. refineries, sewers, etc.). Indeed, there is a growing industry of actuated, actively driven mobile robots that are used to inspect pipes. Many rely on a remote operator to inspect data from a fisheye camera to perform manual inspection and provide no localization or mapping capability. In this work, we introduce a visual odometry-based system using calibrated fisheye imagery and sparse structured lighting to produce high-resolution 3D textured surface models of the inner pipe wall. Our work extends state-of-the-art visual odometry and mapping for fisheye systems to incorporate weak geometric constraints based on prior knowledge of the pipe components into a sparse bundle adjustment framework. These constraints prove essential for obtaining high-accuracy solutions given the limited spatial resolution of the fisheye system and challenging raw imagery. We show that sub-millimeter resolution modeling is viable even in pipes which are 400\u2009mm (16\u201d) in diameter, and that sparse range measurements from a structured lighting solution can be used to avoid the inevitable monocular scale drift. Our results show that practical, high-accuracy pipe mapping from a single fisheye camera is within reach. <\/jats:p>","DOI":"10.1177\/0278364914550133","type":"journal-article","created":{"date-parts":[[2014,11,13]],"date-time":"2014-11-13T04:06:35Z","timestamp":1415851595000},"page":"532-558","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":56,"title":["Visual mapping for natural gas pipe inspection"],"prefix":"10.1177","volume":"34","author":[{"given":"Peter","family":"Hansen","sequence":"first","affiliation":[{"name":"Carnegie Mellon University Qatar, Doha, Qatar"}]},{"given":"Hatem","family":"Alismail","sequence":"additional","affiliation":[{"name":"National Robotics Engineering Center, Robotics Institute, Carnegie Mellon University, PA, USA"}]},{"given":"Peter","family":"Rander","sequence":"additional","affiliation":[{"name":"National Robotics Engineering Center, Robotics Institute, Carnegie Mellon University, 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