3D Point Cloud to BIM: Semi-Automated Framework to Define IFC Alignment Entities from MLS-Acquired LiDAR Data of Highway Roads
Abstract
:1. Introduction
- (1)
- A point cloud-processing method that extracts the road main alignment and offset alignment of a highway road. In order to do so, a method for detection and classification of solid and dashed road markings is also presented. Note that this road marking processing method does not aim to be a contribution by itself, but it is essential for the whole workflow and will be validated to prove that it has state-of-the-art performance.
- (2)
- A conversion of the main alignment and offset alignment as exported from the point cloud processing method, to an IFC Alignment model, which is part of IFC 4.1 standard. The model is supported with UML diagrams.
2. Materials and Methods
2.1. Case Study Data
2.2. Methodology
2.2.1. Ground Segmentation
2.2.2. Road Markings Detection
- The intensity of a point is an attribute that depends on the distance between the sensor and the point itself. Therefore, usage of global intensity thresholds is not feasible. Instead, the intensity attribute should be analyzed locally, among points with similar distance with respect to the sensor.
- Most of the markings are linear elements that follow the direction of the vehicle trajectory (solid and dashed lines). Therefore, it seems convenient to locally search for road markings in a set of slices parallel to the trajectory.
- The generation of those slices needs to have into account the curvature of the road. The longer the slice in the direction of the trajectory at a point, the larger the effect of the curvature of the road. Hence, it is preferable to define short slices and process them iteratively.
2.2.3. Road Markings Processing
- The main objective of the whole process is to extract the center of the road and each lane using the information given by the road markings. Therefore, the relevant markings to be classified are solid and dashed lines.
- The knowledge about the semantics of the road markings will allow to analyze the presence of false positives as well as occlusions and other false negatives on the point cloud of detected road markings, .
2.2.4. Road Edge Detection
- The geometric data that are exported to build the IFC alignment model must not contain any error, so the model can be created correctly. This will be ensured if road edges are detected with no errors.
- A fully automated approach is not desirable for this module. Even if complex heuristics are defined, it is not possible to ensure that road edges are correctly detected in all cases. Therefore, an efficient approach would include an automated process with manual verification, and only in those cases when errors are detected, a manual delineation of road edges would be enabled.
- Manual verification of the results allows the definition of simple heuristics that are able to efficiently detect road edges in most cases, even if they are not robust enough to perform correctly in all cases.
2.2.5. Alignment and Road Lane Processing
- The first necessary step is to detect the number of road lanes. This number is not constant along the study area, and a single point cloud may have different numbers of lanes when, for instance, there is a highway entrance or exit.
- Both solid and dashed markings can separate two lanes. However, the separation between the same lines can change along the road (for instance, a dashed line can be replaced by a solid line for a road section with prohibition of overtaking).
- OffsetXY: The perpendicular distance between each point in and each middle point of each road line.
- OffsetZ: The vertical distance between each point in and each middle point of each road line.
- Offset_id: Since there may be more than one lane per point in , an index is stored for each pair of (OffsetXY, OffsetZ) that points to the coordinate in from which the offsets were obtained, allowing the offset alignment generation as explained in Section 2.2.6.
2.2.6. IFC Alignment Model Generation
3. Results
3.1. Parameters
3.2. Road Marking Detection and Processing
3.3. Alignment and Road Lane Processing
3.4. IFC Alignment Model Generation
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
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Property | Description |
---|---|
path | Path of the point cloud |
indices | Indices of the marking in |
points | Nx3 array with the coordinates of the marking |
class | Class of the marking (solid, dashed, or others) |
geometry | Geometric properties of the marking |
line | Polynomic parametrization of the marking |
Parameter | Value | Parameter | Value |
---|---|---|---|
0.05 m | 3.5 m | ||
0.05 | 10º | ||
1 m | 20 m | ||
0.15 m | 0.75 m | ||
15 | 0.75 m | ||
5 | 𝛼𝑡ℎ | 15º | |
0.5 | 50 m | ||
0.2 m | 0.999 | ||
0.98 | 1 m | ||
0.5 m | 0.5 m | ||
7.5 m | 100 m |
Precision | Recall | F-score | ||
---|---|---|---|---|
0.919 | 0.964 | 0.932 | 0.184 | 0.333 |
GT/Prediction | Solid Line | Dashed Line |
---|---|---|
Solid Line | 99826 | 163 |
Dashed Line | 466 | 13805 |
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Soilán, M.; Justo, A.; Sánchez-Rodríguez, A.; Riveiro, B. 3D Point Cloud to BIM: Semi-Automated Framework to Define IFC Alignment Entities from MLS-Acquired LiDAR Data of Highway Roads. Remote Sens. 2020, 12, 2301. https://doi.org/10.3390/rs12142301
Soilán M, Justo A, Sánchez-Rodríguez A, Riveiro B. 3D Point Cloud to BIM: Semi-Automated Framework to Define IFC Alignment Entities from MLS-Acquired LiDAR Data of Highway Roads. Remote Sensing. 2020; 12(14):2301. https://doi.org/10.3390/rs12142301
Chicago/Turabian StyleSoilán, Mario, Andrés Justo, Ana Sánchez-Rodríguez, and Belén Riveiro. 2020. "3D Point Cloud to BIM: Semi-Automated Framework to Define IFC Alignment Entities from MLS-Acquired LiDAR Data of Highway Roads" Remote Sensing 12, no. 14: 2301. https://doi.org/10.3390/rs12142301