Model-based furniture recognition for building semantic object maps

Günther, Martin; Wiemann, Thomas; Albrecht, Sven; Hertzberg, Joachim

doi:10.1016/j.artint.2014.12.007

Cited by 29 publications

(18 citation statements)

References 34 publications

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“…For example, Johnson et al [55] use this method in power plants for recognizing objects such as machinery, pipes and valves. Günther et al [54] are interested in furniture recognition in an indoor environment. Based on a semantic model of furniture objects, they generate hypotheses to locate object instances and they verify them by matching a geometric model of the object into the point cloud.…”

Section: Object Recognition and Relationship Establishmentmentioning

confidence: 99%

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From Point Clouds to Building Information Models: 3D Semi-Automatic Reconstruction of Indoors of Existing Buildings

2017

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Abstract:The creation of as-built Building Information Models requires the acquisition of the as-is state of existing buildings. Laser scanners are widely used to achieve this goal since they permit to collect information about object geometry in form of point clouds and provide a large amount of accurate data in a very fast way and with a high level of details. Unfortunately, the scan-to-BIM (Building Information Model) process remains currently largely a manual process which is time consuming and error-prone. In this paper, a semi-automatic approach is presented for the 3D reconstruction of indoors of existing buildings from point clouds. Several segmentations are performed so that point clouds corresponding to grounds, ceilings and walls are extracted. Based on these point clouds, walls and slabs of buildings are reconstructed and described in the IFC format in order to be integrated into BIM software. The assessment of the approach is proposed thanks to two datasets. The evaluation items are the degree of automation, the transferability of the approach and the geometric quality of results of the 3D reconstruction. Additionally, quality indexes are introduced to inspect the results in order to be able to detect potential errors of reconstruction.

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Section: Object Recognition and Relationship Establishmentmentioning

confidence: 99%

“…They are used whether in urban environments [52], industrial environments [53] or in indoor areas of buildings [54]. The recognition is based on a database of objects which have to be recognized.…”

Section: Object Recognition and Relationship Establishmentmentioning

confidence: 99%

From Point Clouds to Building Information Models: 3D Semi-Automatic Reconstruction of Indoors of Existing Buildings

2017

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“…A set of planar structures to represent pieces of furniture was presented in [14], which stated that their planar representations "have a certain size, orientation, height above ground and spatial relation to each other". This method was used in [15] to create semantic maps of furniture. This method is similar to our framework; however, their method used a set of rules, while our method uses a probabilistic framework.…”

Section: Related Workmentioning

confidence: 99%

A Graph Representation Composed of Geometrical Components for Household Furniture Detection by Autonomous Mobile Robots

et al. 2018

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This study proposes a framework to detect and recognize household furniture using autonomous mobile robots. The proposed methodology is based on the analysis and integration of geometric features extracted over 3D point clouds. A relational graph is constructed using those features to model and recognize each piece of furniture. A set of sub-graphs corresponding to different partial views allows matching the robot's perception with partial furniture models. A reduced set of geometric features is employed: horizontal and vertical planes and the legs of the furniture. These features are characterized through their properties, such as: height, planarity and area. A fast and linear method for the detection of some geometric features is proposed, which is based on histograms of 3D points acquired from an RGB-D camera onboard the robot. Similarity measures for geometric features and graphs are proposed, as well. Our proposal has been validated in home-like environments with two different mobile robotic platforms; and partially on some 3D samples of a database.

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“…Several object recognition systems can be found in the literature relying on geometric or appearance features of objects, like SIFT based approaches [13], bag of features models [14] or methods based on CAD model matching [15]. However, these methods yield ambiguous results under some circumstances, a drawback that can be alleviated with the use of contextual information [1].…”

Section: Related Workmentioning

confidence: 99%

Online context-based object recognition for mobile robots

Ruiz-Sarmiento

Günther²,

Galindo

et al. 2017

2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

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Abstract-This work proposes a robotic object recognition system that takes advantage of the contextual information latent in human-like environments in an online fashion. To fully leverage context, it is needed perceptual information from (at least) a portion of the scene containing the objects of interest, which could not be entirely covered by just an one-shot sensor observation. Information from a larger portion of the scenario could still be considered by progressively registering observations, but this approach experiences difficulties under some circumstances, e.g. limited and heavily demanded computational resources, dynamic environments, etc. Instead of this, the proposed recognition system relies on an anchoring process for the fast registration and propagation of objects' features and locations beyond the current sensor frustum. In this way, the system builds a graphbased world model containing the objects in the scenario (both in the current and previously perceived shots), which is exploited by a Probabilistic Graphical Model (PGM) in order to leverage contextual information during recognition. We also propose a novel way to include the outcome of local object recognition methods in the PGM, which results in a decrease in the usually high CRF learning complexity. A demonstration of our proposal has been conducted employing a dataset captured by a mobile robot from restaurant-like settings, showing promising results.

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Model-based furniture recognition for building semantic object maps

Cited by 29 publications

References 34 publications

From Point Clouds to Building Information Models: 3D Semi-Automatic Reconstruction of Indoors of Existing Buildings

From Point Clouds to Building Information Models: 3D Semi-Automatic Reconstruction of Indoors of Existing Buildings

A Graph Representation Composed of Geometrical Components for Household Furniture Detection by Autonomous Mobile Robots

Online context-based object recognition for mobile robots

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