Incremental 3D Cuboid Modeling with Drift Compensation
This paper presents a framework of incremental 3D cuboid modeling by using the mapping results of an RGB-D camera based simultaneous localization and mapping (SLAM) system. This framework is useful in accurately creating cuboid CAD models from a point cloud in an online manner. While performing the...
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doaj-d0acf065dffd40a68a41246181ee4d232020-11-25T01:22:05ZengMDPI AGSensors1424-82202019-01-0119117810.3390/s19010178s19010178Incremental 3D Cuboid Modeling with Drift CompensationMasashi Mishima0Hideaki Uchiyama1Diego Thomas2Rin-ichiro Taniguchi3Rafael Roberto4João Paulo Lima5Veronica Teichrieb6Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, JapanLibrary, Kyushu University, Fukuoka 819-0395, JapanFaculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, JapanFaculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, JapanVoxar Labs, Centro de Informática, Universidade Federal de Pernambuco, Recife 50740-560, BrazilVoxar Labs, Centro de Informática, Universidade Federal de Pernambuco, Recife 50740-560, BrazilVoxar Labs, Centro de Informática, Universidade Federal de Pernambuco, Recife 50740-560, BrazilThis paper presents a framework of incremental 3D cuboid modeling by using the mapping results of an RGB-D camera based simultaneous localization and mapping (SLAM) system. This framework is useful in accurately creating cuboid CAD models from a point cloud in an online manner. While performing the RGB-D SLAM, planes are incrementally reconstructed from a point cloud in each frame to create a plane map. Then, cuboids are detected in the plane map by analyzing the positional relationships between the planes, such as orthogonality, convexity, and proximity. Finally, the position, pose, and size of a cuboid are determined by computing the intersection of three perpendicular planes. To suppress the false detection of the cuboids, the cuboid shapes are incrementally updated with sequential measurements to check the uncertainty of the cuboids. In addition, the drift error of the SLAM is compensated by the registration of the cuboids. As an application of our framework, an augmented reality-based interactive cuboid modeling system was developed. In the evaluation at cluttered environments, the precision and recall of the cuboid detection were investigated, compared with a batch-based cuboid detection method, so that the advantages of our proposed method were clarified.http://www.mdpi.com/1424-8220/19/1/178geometric shapecuboidincrementally structural modelingpoint cloud |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Masashi Mishima Hideaki Uchiyama Diego Thomas Rin-ichiro Taniguchi Rafael Roberto João Paulo Lima Veronica Teichrieb |
spellingShingle |
Masashi Mishima Hideaki Uchiyama Diego Thomas Rin-ichiro Taniguchi Rafael Roberto João Paulo Lima Veronica Teichrieb Incremental 3D Cuboid Modeling with Drift Compensation Sensors geometric shape cuboid incrementally structural modeling point cloud |
author_facet |
Masashi Mishima Hideaki Uchiyama Diego Thomas Rin-ichiro Taniguchi Rafael Roberto João Paulo Lima Veronica Teichrieb |
author_sort |
Masashi Mishima |
title |
Incremental 3D Cuboid Modeling with Drift Compensation |
title_short |
Incremental 3D Cuboid Modeling with Drift Compensation |
title_full |
Incremental 3D Cuboid Modeling with Drift Compensation |
title_fullStr |
Incremental 3D Cuboid Modeling with Drift Compensation |
title_full_unstemmed |
Incremental 3D Cuboid Modeling with Drift Compensation |
title_sort |
incremental 3d cuboid modeling with drift compensation |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2019-01-01 |
description |
This paper presents a framework of incremental 3D cuboid modeling by using the mapping results of an RGB-D camera based simultaneous localization and mapping (SLAM) system. This framework is useful in accurately creating cuboid CAD models from a point cloud in an online manner. While performing the RGB-D SLAM, planes are incrementally reconstructed from a point cloud in each frame to create a plane map. Then, cuboids are detected in the plane map by analyzing the positional relationships between the planes, such as orthogonality, convexity, and proximity. Finally, the position, pose, and size of a cuboid are determined by computing the intersection of three perpendicular planes. To suppress the false detection of the cuboids, the cuboid shapes are incrementally updated with sequential measurements to check the uncertainty of the cuboids. In addition, the drift error of the SLAM is compensated by the registration of the cuboids. As an application of our framework, an augmented reality-based interactive cuboid modeling system was developed. In the evaluation at cluttered environments, the precision and recall of the cuboid detection were investigated, compared with a batch-based cuboid detection method, so that the advantages of our proposed method were clarified. |
topic |
geometric shape cuboid incrementally structural modeling point cloud |
url |
http://www.mdpi.com/1424-8220/19/1/178 |
work_keys_str_mv |
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