{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T20:19:38Z","timestamp":1768508378116,"version":"3.49.0"},"reference-count":45,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T00:00:00Z","timestamp":1768089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council","award":["NSTC 111-2221-E-005-031-"],"award-info":[{"award-number":["NSTC 111-2221-E-005-031-"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes an optical three-dimensional (3D) point cloud acquisition and sketching system, which is not limited by the measurement size, unlike traditional 3D object measurement techniques. The system employs an optical displacement sensor for surface displacement scanning and a six-axis inertial sensor (accelerometer and gyroscope) for spatial attitude perception. A microprocessor control unit (MCU) is responsible for acquiring, merging, and calculating data from the sensors, converting it into 3D point clouds. Butterworth filtering and Mahoney complementary filtering are used for sensor signal preprocessing and calculation, respectively. Furthermore, a human\u2013machine interface is designed to visualize the point cloud and display the scanning path and measurement trajectory in real time. Compared to existing works in the literature, this system has a simpler hardware architecture, more efficient algorithms, and better operation, inspection, and observation features. The experimental results show that the maximum measurement error on 2D planes is 4.7% with a root mean square (RMS) error of 2.1%, corresponding to the reference length of 10.3 cm. For 3D objects, the maximum measurement error is 5.3% with the RMS error of 2.4%, corresponding to the reference length of 9.3 cm. Finally, it was verified that this system can also be applied to large-sized 3D objects for outlines.<\/jats:p>","DOI":"10.3390\/s26020476","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T08:20:37Z","timestamp":1768206037000},"page":"476","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Low-Cost Optical\u2013Inertial Point Cloud Acquisition and Sketch System"],"prefix":"10.3390","volume":"26","author":[{"given":"Tung-Chen","family":"Chao","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, National Chung Hsing University, Taichung 40227, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4013-8605","authenticated-orcid":false,"given":"Hsi-Fu","family":"Shih","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Chung Hsing University, Taichung 40227, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5610-6010","authenticated-orcid":false,"given":"Chuen-Lin","family":"Tien","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Feng Chia University, Taichung 40724, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han-Yen","family":"Tu","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Chinese Culture University, Taipei 11114, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1007\/s00138-016-0784-4","article-title":"An overview of depth cameras and range scanners based on time-of-flight technologies","volume":"27","author":"Horaud","year":"2016","journal-title":"Mach. 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