Live Structural Modeling Using RGB-D SLAM

Olivier, Nicolas; Uchiyama, Hideaki; Mishima, Masashi; Thomas, Diego; Roberto, Rafael; Lima, João Paulo; Teichrieb, Verônica

doi:10.1109/icra.2018.8460973

Cited by 6 publications

(13 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As examples of the lab effort in long term research, two are very representative: the 3D Tracking area and the Sicure project ( Figure 5). The 3D Tracking research area has powered international cooperation projects (i.e., INRIA/UChile/ UFPE and TUGraz/Kyushu/UFPE), industry projects and patents, various collaborative PostDoc and Ph.D.'s (e.g., with Microsoft Research, EPFL, TUGraz), research papers and prizes in high quality venues (ISMAR [5] [6], ICRA [7], C&G [8][9], IJCNN [10], Sensors [11]).…”

Section: Representative Projectsmentioning

confidence: 99%

Voxar Labs

Teichrieb

Simões²,

Figueiredo

et al. 2020

Revista CI

Self Cite

View full text Add to dashboard Cite

Voxar Labs is a research group focused in augmenting experiences through research, innovation and collaboration with academia and industry. It develops cutting-edge multi-disciplinary research in the large area of Spatial Computing, tackling the inner areas of Extended Reality, Computer Vision and Natural Interaction. The laboratory aims to create impact through R&D&I, technology transfer, scientific publications, patents and human-resources formation. It is one of the most productive Augmented Reality research groups in the Latin America, also being recognized with seven best papers and ten first-place competitions’ prizes over the nine years of its existence. Voxar Labs is part of the Informatics Center of the Federal University of Pernambuco, located in Recife – Pernambuco, Brazil.

show abstract

Section: Representative Projectsmentioning

confidence: 99%

Voxar Labs

Teichrieb

Simões²,

Figueiredo

et al. 2020

Revista CI

Self Cite

View full text Add to dashboard Cite

show abstract

“…First, a plane map, which is composed of oriented planes, is incrementally created from point clouds, and used as the input to our framework. This incremental mapping process is based on our previous method [21] that applies a shape-detection method [28] to incoming point clouds acquired from an RGB-D SLAM system [29]. In other words, any SLAM system can be used as an external tracker, and incrementally produces a noisy 3D point cloud.…”

Section: Overviewmentioning

confidence: 99%

“…At every frame, the planes in the plane map are classified into planes assigned to cuboids and unassigned ones, as illustrated in Figure 2. First, the cuboid check is performed for the cuboids in the cuboid map to check the positional relationships of the cuboid faces at every frame because their parameters are incrementally updated with [21], This process is specifically referred to as cuboid update. For the unassigned planes, the cuboid check with the cuboid faces in the cuboid map is performed so that the faces in the cuboid map can be replaced with new planes or an undetected cuboid face such as a fourth plane can be assigned to a cuboid in the cuboid map.…”

Section: Overviewmentioning

confidence: 99%

“…We focus on stably modeling cuboids from the incrementally updated point cloud. At every frame, planes are incrementally reconstructed and updated from a point cloud-based map [21], and are used as the input to our framework. Then, planes are clustered to compose cuboids by analyzing three plane-positional relationships, namely, orthogonality, convexity, and proximity.…”

Section: Introductionmentioning

confidence: 99%

“…By determining three perpendicular cuboid edges from both the intersection and the normal vectors of each face, the width, height, and depth of a cuboid are finally computed as cuboid-shape parameters. Since the parameters can be incrementally updated based on Reference [21], the positional relationships of the planes are analyzed at every frame. This means that both newly detected planes and previously detected cuboids are analyzed so that the false detection of the cuboids can be suppressed with this sequential processing.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Incremental 3D Cuboid Modeling with Drift Compensation

Mishima

Uchiyama

Thomas

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract