Autonomous Point Cloud Acquisition of Unknown Indoor Scenes

González-deSantos, Luis Miguel; Díaz-Vilariño, L.; Balado, Jesús; Martínez-Sánchez, J.; González-Jorge, H.; Sánchez‐Rodríguez, Ana

doi:10.3390/ijgi7070250

Cited by 13 publications

(12 citation statements)

References 30 publications

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“…After the visibility analysis, voxels are classified into: 'visible' and 'occluded'. A voxel is considered 'occupied' if it contains a minimum number of 20 points as in (González-deSantos et al, 2018). Figure 8 shows the results in terms of three-dimensional representation of space view accomplished for Test A (central visual angle, and mid-peripheral visual angle).…”

Section: Experiments and Resultsmentioning

confidence: 99%

From Point Clouds to 3d Isovists in Indoor Environments

Díaz-Vilariño

González-deSantos

Verbree

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Visibility is a common measure to describe the spatial properties of an environment related to the spatial behaviour. Isovists represent the space that can be seen from one observation point, and they are used to analyse the existence of obstacles affecting or blocking intervisibility in an area. Although point clouds depict the as-built reality in a very detailed and accurate way, literature addressing the analysis of visibility in 3D, and more specifically the usage of point clouds to visibility analysis, is rather limited. In this paper, a methodology to evaluate visibility from point clouds in indoor environments is proposed, resulting in the creation of 3D isovists. Point cloud is firstly discretized in a voxel-based structure and voxels are labelled into ‘exterior’, ‘occupied’, ‘visible’ and ‘occluded’ based on an occupancy followed by a visibility analysis performed from a ray-tracing algorithm. 3D Isovists are created from the boundary of visible voxels from an observer position and considering as input parameters the visual angle, maximum line of sight, and eye gaze direction.</p>

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Section: Experiments and Resultsmentioning

confidence: 99%

From Point Clouds to 3d Isovists in Indoor Environments

Díaz-Vilariño

González-deSantos

Verbree

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…This UGV has a payload of 50kg and an autonomy of three hours. Similar systems have been previously used for this purpose [ 23 , 24 , 27 ]. This UGV makes use of SLAM algorithms for autonomous navigation, thus this manuscript will focus on NBV calculation.…”

Section: Methodsmentioning

confidence: 99%

“…This grid is treated like an image, using a pattern to look for rectangular holes that may be a door/window, using the method presented by González-de Santos et al . [ 27 ]. For this study case only rectangular windows are considered, for other types of windows, such as circular or oval windows it would be necessary to define different patterns.…”

Section: Methodsmentioning

confidence: 99%

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New discretization method applied to NBV problem: Semioctree

et al. 2018

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This paper presents a discretization methodology applied to the NBV (Next Best View) problem, which consists of determining the heuristical best position of the next scan. This new methodology is a hybrid process between a homogenous voxelization and an octree structure that preserves the advantages of both methods. An octree structure is not directly applicable to the NBV problem: as the point cloud grows with every successive scanning, the limits and position of the discretization, octree structure must coincide, in order to transfer the information from one scan to the next. This problem is solved by applying a first coarse voxelization, followed by the division of each voxel in an octree structure. In addition, a previous methodology for solving the NBV problem has been adapted to make use of this novel approach. Results show that the new method is three times faster than the homogenous voxelization for a maximum resolution of 0.2m. For this target resolution of 0.2m, the number of voxels/octants in the discretization is reduced approximately by a 400%, from 35.360 to 8.937 for the study case presented.

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“…The next scan position is chosen according to the probabilities calculated in the previous step. González-de Santos et al [30] extend previous approaches to also consider windows and doors, which are identified through a visibility analysis. In this way, once acquisition is completed for one room, the existence of doors determines subsequent next best scans.…”

Section: Scan-planning In Indoor Environmentsmentioning

confidence: 99%

From BIM to Scan Planning and Optimization for Construction Control

et al. 2019

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Scan planning of buildings under construction is a key issue for an efficient assessment of work progress. This work presents an automatic method aimed to determinate the optimal scan positions and the optimal route based on the use of Building Information Models (BIM) and considering data completeness as stopping criteria. The method is considered for a Terrestrial Laser Scanner mounted on a mobile robot following a stop & go procedure. The method starts by extracting floor plans from the BIM model according to the planned construction status, and including geometry and semantics of the building elements considered for construction control. The navigable space is defined from a binary map considering a security distance to building elements. After a grid-based and a triangulation-based distribution are implemented for generating scan position candidates, a visibility analysis is carried out to determine the optimal number and position of scans. The optimal route to visit all scan positions is addressed by using a probabilistic ant colony optimization algorithm. The method has been tested in simulated and real buildings under very dissimilar conditions and structural construction elements. The two approaches for generating scan position candidates are evaluated and results show the triangulation-based distribution as the more efficient approach in terms of processing and acquisition time, especially for large-scale buildings.

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Autonomous Point Cloud Acquisition of Unknown Indoor Scenes

Cited by 13 publications

References 30 publications

From Point Clouds to 3d Isovists in Indoor Environments

From Point Clouds to 3d Isovists in Indoor Environments

New discretization method applied to NBV problem: Semioctree

From BIM to Scan Planning and Optimization for Construction Control

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