# Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites

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## Abstract

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## 1. Introduction

- Outliers exist in construction site point clouds due to data artefacts, occlusions and dust.
- Many object-based recognition models depend on the 3D/4D planned building information model (BIM), which is neither readily available nor accurate, and the as-built is not necessarily constructed to plan. The compliance of the as-built to the planned specifications, must in fact be checked through the monitoring and control process; hence, a monitoring and control process solely reliant on the details of the planned is not desirable.
- Most planar and linear classification and segmentation algorithms use inconsistent and subjectively-defined thresholds (see Section 2.2.2), which change from one dataset to another, and hence, are not generalizable for every environment.

## 2. Literature Review

#### 2.1. TLS in Construction Management

#### 2.1.1. Application of TLS to Measure Key Performance Indicators

#### 2.1.2. State-of-the-Art in Automated TLS Object Extraction

#### 2.2. Automated Classification and Segmentation of Planar and Linear Features from TLS Point Clouds

- Planar (linear) classification: The process of extracting points that locally follow a planar (linear) pattern within a point cloud dataset. In other words, points that locally follow a planar (linear) pattern are classified as planar (linear) regardless of their specific parametric equation.
- Planar (linear) segmentation: The process of grouping the classified planar (linear) points that follow a similar planar (linear) parametric equation. In other words, points that are globally on the same plane (line).

#### 2.2.1. Robust PCA-Based Point Cloud Classification

#### 2.2.2. Planar and Linear Segmentation

## 3. Methodology

#### 3.1. Robust Planar and Linear Classification

#### 3.1.1. Neighbourhood Definition

#### 3.1.2. Robust PCA

#### 3.2. Robust Planar and Linear Segmentation

#### 3.2.1. Attribute Definition

#### 3.2.2. New Iterative Complete Linkage for Point Cloud Clustering

#### 3.2.3. Systematic Choice of Initial Similarity Threshold

#### 3.2.4. Robust Complete Linkage

#### 3.2.5. Spatial Continuity of Clusters

#### 3.3. Robust Extraction of Flat Slab Floors

#### 3.4. Method of Validation of Results

## 4. Experiment Description

#### 4.1. Experiment 1: Mechanics of Material Laboratory

#### 4.2. Experiment 2: Graduate Student Hall of Residence Construction Site

#### 4.3. Experiment 3: Taylor Institute of Teaching and Learning Construction Site

## 5. Experimental Results

#### 5.1. Experiment 1: Mechanics of Materials Laboratory

#### 5.1.1. Robust Floor and Ceiling Extraction

#### 5.1.2. Robust Planar Classification and Segmentation

#### 5.2. Experiment 2: Graduate Student Hall of Residence

#### 5.2.1. Epoch 1: Linear Segmentation Results

#### 5.2.2. Epoch 1: Comparative Evaluation of Rebar Segmentation Using Robust Complete Linkage

#### 5.2.3. Epochs 1–6: Robust Floor Extraction

#### 5.2.4. Epochs 2–6: Robust Planar and Linear Segmentation

#### 5.3. Experiment 3: Taylor Institute of Teaching and Learning

#### 5.3.1. Stairs: Robust Planar Segmentation

#### 5.3.2. Internal Truss: Robust Planar Segmentation

#### 5.3.3. Evaluation of H-Section Web Segmentation Using Robust Complete Linkage

## 6. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 3.**The 95th percentile of the percentage of relative error value for: (

**a**) each axis of the directional and projection vector of the simulated linear points; and (

**b**) each axis of the normal vector and the projection distance of the simulated planar points.

**Figure 4.**2D representation of the proposed segmentation framework (each coloured circle represents a segment): (

**a**) iterative complete linkage; and (

**b**) robust complete linkage.

**Figure 6.**(

**a**) Expected point height distribution; (

**b**) schematic representation of the raw measurements of a point on a horizontal plane.

**Figure 8.**(

**a**) Registered point clouds acquired from the MML (left, outer view; right, sliced view of the inside); (

**b**) histogram of point height of the point cloud acquired from the MML; and (

**c**) results of the automatic floor and ceiling extraction (each colour represents a different segment).

**Figure 9.**Planar classification of MML point cloud: (

**a**) using only predefined thresholds of Equation (3); and (

**b**) using outlier detection (Det-MCD) on the normalized eigenvalues of classified points of (

**a**). (

**c**) Robust planar segmentation of the classified planar points of (

**b**).

**Figure 10.**(

**a**) Point cloud captured from Epoch 1 (colour represents intensity); (

**b**) robust planar and linear segmentation results (each colour represents one segment); (

**c**) rebar segmentation before and after the robust complete linkage algorithm; and (

**d**) top-view of the rebar (linear feature) segmentation of the elevator shaft.

**Figure 11.**(

**a**) Point cloud of one of the columns; (

**b**) planar and linear segmentation results using our method; and (

**c**) planar and linear segmentation results using region growing of [47] with robust PCA. Linear segmentation results of the rebars using: (

**d**) Rabbani et al. region growing with robust PCA classification; and (

**e**) our method. (

**f**) Classified linear points: (left) Cartesian coordinates; and (right) coordinates of the robust centres.

**Figure 12.**(

**a**) The point clouds of the first four floors (all six epochs) registered to the reference coordinate system; and (

**b**) histogram of point height of all of the scans combined; planar and linear segmentation results (top and side views) from the accumulated points of: (

**c**) Epochs 1–2; (

**d**) Epochs 1–3; (

**e**) Epochs 1–4; (

**f**) Epochs 1–5; and (

**g**) Epochs 1–6.

**Figure 13.**(

**a**) point cloud of the TITL building during construction; and (

**b**) result of the robust planar segmentation of the whole site.

**Figure 14.**(

**a**) Point cloud of the staircase; (

**b**) robust planar segmentation results of the staircase; (

**c**) point cloud of the random subset of (

**a**); and (

**d**) planar segmentation results of the point cloud of (

**c**).

**Figure 15.**(

**a**) Results of the segmentation of the internal truss frame consisting of H-section elements. Results of the robust planar segmentation of the H-section beams of the top of the main truss: (

**b**) case where the two surfaces were correctly identified; (

**c**) case where the high level of noise did not allow for correct differentiation of the surfaces; and (

**d**) case where the density of the accumulated points on one surface did not allow for the correct segmentation of the two faces of the planar web.

**Figure 16.**(

**a**) Point cloud of a sample H-section column; (

**b**) points classified as planar using robust PCA; (

**c**) the robust centres of the planar classified points; (

**d**) planar segmentation results using [47] method; and (

**e**) planar segmentation results using our proposed method.

**Table 1.**# of scan-stations, average # of targets per scan-station and registration precision of the all datasets.

Experiment | Epoch | # of Scan-Stations | Total # of Points (millions) | Average # of Targets per Scan Location | Registration Precision (mm) |
---|---|---|---|---|---|

Experiment 1: MML | 1 | 3 | 30 | 4 | 1.2 |

Experiment 2: GSHR | 1 | 3 | 37 | 7 | 1.5 |

2 | 3 | 153 | 6 | 1.4 | |

3 | 4 | 201 | 8 | 2.2 | |

4 | 3 | 115 | 8 | 1.5 | |

5 | 5 | 358 | 6 | 1.8 | |

6 | 3 | 128 | 7 | 1.2 | |

Experiment 3: TITL | 1 | 6 | 537 | 5 | 1.7 |

Planar Classification Results | Linear Classification Results | Overall | |||||||

Precision | Recall | Accuracy | Precision | Recall | Accuracy | Precision | Recall | Accuracy | |

Epoch 1 | 95.0 | 94.6 | 91.1 | 92.6 | 91.6 | 98.4 | 94.7 | 94.3 | 94.7 |

Epoch 2 | 95.4 | 93.9 | 91.0 | 90.9 | 90.9 | 97.7 | 94.7 | 93.5 | 94.6 |

Epoch 3 | 93.7 | 95.2 | 91.1 | 93.1 | 92.2 | 98.6 | 93.7 | 94.9 | 94.8 |

Epoch 4 | 94.5 | 96.4 | 92.7 | 90.1 | 94.1 | 97.8 | 93.7 | 96.0 | 95.4 |

Epoch 5 | 96.5 | 94.3 | 92.1 | 93.0 | 93.4 | 98.5 | 96.0 | 94.2 | 95.5 |

Epoch 6 | 94.5 | 93.0 | 92.3 | 92.5 | 91.0 | 95.1 | 93.9 | 92.4 | 93.7 |

Overall | 95.1 | 94.8 | 91.8 | 91.7 | 92.4 | 97.8 | 94.6 | 94.4 | 95.0 |

Planar Segmentation Results | Linear Segmentation Results | Overall | |||||||

Precision | Recall | Accuracy | Precision | Recall | Accuracy | Precision | Recall | Accuracy | |

Epoch 1 | 97.6 | 98.1 | 96.0 | 95.7 | 97.3 | 93.7 | 97.4 | 98.1 | 95.8 |

Epoch 2 | 98.3 | 99.7 | 98.1 | 96.1 | 99.3 | 95.7 | 98.0 | 99.6 | 97.7 |

Epoch 3 | 97.2 | 98.1 | 95.6 | 95.4 | 95.6 | 92.0 | 97.0 | 97.9 | 95.2 |

Epoch 4 | 96.4 | 97.1 | 93.9 | 97.1 | 98.9 | 96.2 | 96.5 | 97.4 | 94.3 |

Epoch 5 | 98.1 | 99.8 | 98.0 | 95.8 | 99.7 | 95.7 | 97.8 | 99.8 | 97.7 |

Epoch 6 | 96.9 | 97.2 | 94.7 | 94.9 | 98.5 | 93.9 | 96.2 | 97.6 | 94.4 |

Overall | 97.5 | 98.5 | 96.2 | 96.0 | 98.7 | 95.0 | 97.2 | 98.6 | 96.1 |

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## Share and Cite

**MDPI and ACS Style**

Maalek, R.; Lichti, D.D.; Ruwanpura, J.Y. Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites. *Sensors* **2018**, *18*, 819.
https://doi.org/10.3390/s18030819

**AMA Style**

Maalek R, Lichti DD, Ruwanpura JY. Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites. *Sensors*. 2018; 18(3):819.
https://doi.org/10.3390/s18030819

**Chicago/Turabian Style**

Maalek, Reza, Derek D Lichti, and Janaka Y Ruwanpura. 2018. "Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites" *Sensors* 18, no. 3: 819.
https://doi.org/10.3390/s18030819