From BIM to Scan Planning and Optimization for Construction Control
"> Figure 1
<p>General workflow of the method.</p> "> Figure 2
<p>(<b>a</b>) Building elements considered in this work, (<b>b</b>) elements after discretization in equidistant points.</p> "> Figure 3
<p>Geometric representation of navigable space (<b>a</b>) in case of the existence of holes and columns (blue lines), (<b>b</b>) in case of the existence of rooms.</p> "> Figure 4
<p>Schema of discretization process when using a grid-based structure (<b>a</b>) in case of the existence of one floor space including a hole and three columns (blue lines) (<b>b</b>) in case of the existence of rooms. Final candidate positions are in green, while discarded candidate positions are in red. Local coordinate systems are represented.</p> "> Figure 5
<p>Workflow of the triangulation process.</p> "> Figure 6
<p>Generation of seed points S<sub>e</sub> (in blue) for triangulation process for: (<b>a</b>) column, (<b>b</b>) polygon with side < <span class="html-italic">d<sub>min</sub></span> and (<b>c</b>) polygon with side < <span class="html-italic">d<sub>min</sub></span>.</p> "> Figure 7
<p>Navigable space is partitioned by applying Delaunay triangulation process. (<b>a</b>) Since the polygon that contains navigable space may be concave, some positions obtained after the division can fall out of navigable space (orange). Small triangles are discarded according the parameters <span class="html-italic">l<sub>min</sub></span> and <span class="html-italic">α<sub>min</sub></span> (yellow). (<b>b</b>) A filtering process is conducted to retrieve positions are inside navigable space (points generated by Voronoi process are included). Subsequently, points near obstacles are discarded (red) according to the defined security distance.</p> "> Figure 8
<p>Bresenham algorithm is used to determinate the map cells that are crossed by simulated beam (gray) in visibility analysis. (<b>a</b>) target cell (red) is wrongly classified as visible since it is not occluded by other cells representing building elements, (<b>b</b>) target point (green) is correctly classified as occluded in the ray-casting process.</p> "> Figure 9
<p>Scan optimization process is carried out from candidate positions (green points) to obtain scanning positions (red points). In each iteration the candidate position is selected which maximizes the theoretically acquirable surface (red lines). The black lines represent the surface theoretically acquired from the previously selected positions.</p> "> Figure 10
<p>(<b>a</b>) Graph nodes are 8-connected by edges and the ones intersecting with any no-navigable space are removed (magenta). (<b>b</b>) Scanning positions (red points) are relocated to nearest nodes to them (blue points with red contour).</p> "> Figure 11
<p>(<b>a</b>) A navigable graph composed by navigable (blue) and scanning (red) nodes is generated. Then, navigable nodes are abstracted from graph and (<b>b</b>) a simpler one is represented only with scanning nodes (red).</p> "> Figure 12
<p>(<b>a</b>) A subgraph is generated separately for each room. (<b>b</b>) The global graph consists of all subgraphs joined by new nodes corresponding to door positions (yellow).</p> "> Figure 13
<p>(<b>a</b>) Input BIM model. (b) DXF model of case study 1 and (<b>b</b>) DXF model of case study 2.</p> "> Figure 14
<p>Candidate positions generated by both discretization methods in case study 1: (<b>a</b>) grid-based method in structural phase, (<b>b</b>) triangulation-based method in structural phase, (<b>c</b>) grid-based method with rooms and (<b>d</b>) triangulation-based method with rooms. Horizontal and vertical elements are displayed in magenta and black respectively. Green points represent position reachable by robotic system, unreachable positions are depicted in red.</p> "> Figure 15
<p>Candidate positions generated by both discretization methods in case study 2.</p> "> Figure 16
<p>Visibility analysis results obtained from candidates generated in case study 1: (<b>a</b>) grid-based candidate distribution in structural phase, (<b>b</b>) triangulation-based candidate distribution in structural phase, (<b>c</b>) grid-based candidate distribution with rooms and (<b>d</b>) triangulation-based candidate distribution with rooms. Elements determined as visible for analysis process are depicted in green, black points represent no visible elements.</p> "> Figure 17
<p>Visibility analysis results obtained from candidates generated in case study 2.</p> "> Figure 18
<p>Visibility analysis of case study 1 original (<b>a</b>) and rotated (<b>b</b>). Visible elements are coloured in green, while black zones correspond to areas of element to be acquired are not visible from any candidate position.</p> "> Figure 19
<p>Optimization scan position result in case study 1: (<b>a</b>) grid-based method, (<b>b</b>) triangulation-based method, (<b>c</b>) grid-based with scan positions in doors and (<b>d</b>) triangulation-based method with scan positions in doors. Color code: scan positions (red points), candidates scan positions (gray points), acquired elements (green), non-acquired elements (black).</p> "> Figure 20
<p>Optimization scan position result in case study 2.</p> "> Figure 21
<p>Optimal route calculated in case study 1 whose scanning positions were obtained by: (<b>a</b>) grid-based method in structural phase, (<b>b</b>) triangulation-based method in structural phase, (<b>c</b>) grid-based method with rooms and (<b>d</b>) triangulation-based method with rooms. Horizontal and vertical elements are displayed in magenta and black respectively. Green points represent start and end scanning position, the remaining ones are depicted in red.</p> "> Figure 22
<p>Optimal route calculated in case study 2.</p> "> Figure 23
<p>Optimal route calculated in case study 1 with scan positions at doorways in (<b>a</b>) and without scan position at doorways in (<b>b</b>). Color code: scan positions (red points), graph nodes (gray points), start-end positions (green points), scanning-door positions (orange).</p> "> Figure 24
<p>Workflow of the entire process from Building Information Model (BIM) to the acquired point cloud tracking scanning plan generated by the proposed algorithm.</p> ">
Abstract
:1. Introduction
- To compare two methods of scan position distribution (grid-based and triangular-based) and select the one that shows the most robust behavior.
- To design a method adapted to the building complexity.
- To consider acquiring vertical elements, such as beams, from a two-dimensional perspective.
- To implement a route calculation and optimization method that joins scan positions avoiding obstacles.
2. Related Work
2.1. Scan-Planning in Indoor Environments
2.2. Discretization of the Navigable Space
3. Methodology
3.1. From 3D to 2D
3.2. Discretization of the Navigable Space
3.2.1. Grid-Based Distribution
3.2.2. Triangulation-Based Distribution
3.3. Visibility Analysis
3.4. Scan Optimization
3.5. Optimal Routing
4. Results and Discussion
4.1. Instruments and Data
4.2. Results
4.2.1. Parameters and Values
4.2.2. From 3D to 2D
4.2.3. Distribution of the Navigable Space
4.2.4. Visibility Analysis
4.2.5. Scan Optimization
4.2.6. Optimal Route
4.3. Application in A Real Case Study
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Type of Parameter | Parameter | Abbreviation | Value |
---|---|---|---|
Discretization resolution | ddis | 50 mm | |
Laser range | r | 5 m | |
Field of view | v | 360º | |
General | Security Distance | dsec | 0.7 m |
Coverage | cmin | 90% | |
Specifics | Resolution grid | rgrid | 1.0 m |
Door accessibility | door_access | 0.7 m | |
Doors as scanning position | door_scan | True/False |
Scenario | BEAMS | COLUMNS | STAIRS | WALLS | TOTAL |
---|---|---|---|---|---|
Case 1 (Structural) | 9066 | 1038 | 891 | - | 10995 |
Case 1 (Rooms) | - | 1038 | 891 | 6296 | 8225 |
Case 2 (Structural) | 6044 | 491 | 682 | - | 7217 |
Case 2 (Rooms) | - | 491 | 682 | 5690 | 6863 |
Case of Study | Scenario | Distribution | Number of Candidates | Reachable Candidates | Time (s) |
---|---|---|---|---|---|
Case 1 | Structural | Grid-based | 374 | 247 | 0.83 |
Triangulation-based | 131 | 80 | 0.33 | ||
Rooms | Grid-based | 297 | 163 | 0.83 | |
Triangulation-based | 368 | 229 | 1.06 | ||
Case 2 | Structural | Grid-based | 214 | 182 | 0.80 |
Triangulation-based | 65 | 35 | 1.10 | ||
Rooms | Grid-based | 183 | 105 | 0.79 | |
Triangulation-based | 233 | 138 | 0.68 |
Scenario | Units To Be Acquired | Method | Candidate Positions | Visible Units | Time (s) | Avg. Time (s) |
---|---|---|---|---|---|---|
Case 1 (structural) | 10104 | Grid-based | 247 | 9864 | 15.66 | 0.063 |
Triangulation-based | 80 | 9714 | 4.73 | 0.059 | ||
Case 2 (structural) | 6535 | Grid-based | 182 | 6230 | 7.73 | 0.042 |
Triangulation-based | 35 | 6173 | 1.53 | 0.043 | ||
Case 1 (Rooms) | 7334 | Grid-based | 163 | 3940 | 2.45 | 0.015 |
Triangulation-based | 229 | 4260 | 3.78 | 0.017 | ||
Case 2 (Rooms) | 6181 | Grid-based | 105 | 3193 | 1.69 | 0.016 |
Triangulation-based | 138 | 3417 | 2.18 | 0.016 |
l_rng | 5 m | 10 m | |
---|---|---|---|
Grid-based | units of visible elements | 9864 units | 9967 units |
time consumed | 15.66 s | 89.59 s | |
Triangulation-based | units of visible elements | 9714 units | 9963 units |
time consumed | 4.73 s | 21.18 s |
Scenario | Method | Candidate Positions | Scanning Positions | Acquired (%) | Time (s) |
---|---|---|---|---|---|
Case 1 (structural) | Grid-based | 247 | 10 | 90.67 | 1.94 |
Triangulation-based | 80 | 10 | 90.71 | 0.58 | |
Case 2 (structural) | Grid-based | 182 | 7 | 90.48 | 0.92 |
Triangulation-based | 35 | 7 | 90.07 | 0.17 | |
Case 1 (Rooms) | Grid-based | 163 | 17 | 90.36 | 1.36 |
Triangulation-based | 229 | 19 | 90.71 | 2.19 | |
Case 2 (Rooms) | Grid-based | 105 | 13 | 92.48 | 0.71 |
Triangulation-based | 138 | 14 | 90.31 | 0.97 |
Scenario | Method | Scanning Positions | Time Graph Generation (s) | Route Distance (m) | Time Route Calculation (s) |
---|---|---|---|---|---|
Case 1 (structural) | Grid-based | 10 | 2.35 | 55.61 | 0.15 |
Triangulation-based | 10 | 2.38 | 57.02 | 0.15 | |
Case 2 (structural) | Grid-based | 7 | 1.58 | 42.42 | 0.06 |
Triangulation-based | 7 | 1.55 | 44.59 | 0.05 | |
Case 1 (Rooms) | Grid-based | 17 | 3.02 | 103.99 | 0.63 |
Triangulation-based | 19 | 4.27 | 100.34 | 0.94 | |
Case 2 (Rooms) | Grid-based | 13 | 1.87 | 88.10 | 0.29 |
Triangulation-based | 14 | 1.89 | 87.27 | 0.35 |
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Frías, E.; Díaz-Vilariño, L.; Balado, J.; Lorenzo, H. From BIM to Scan Planning and Optimization for Construction Control. Remote Sens. 2019, 11, 1963. https://doi.org/10.3390/rs11171963
Frías E, Díaz-Vilariño L, Balado J, Lorenzo H. From BIM to Scan Planning and Optimization for Construction Control. Remote Sensing. 2019; 11(17):1963. https://doi.org/10.3390/rs11171963
Chicago/Turabian StyleFrías, Ernesto, Lucía Díaz-Vilariño, Jesús Balado, and Henrique Lorenzo. 2019. "From BIM to Scan Planning and Optimization for Construction Control" Remote Sensing 11, no. 17: 1963. https://doi.org/10.3390/rs11171963