A Multi-Constraint Combined Method for Ground Surface Point Filtering from Mobile LiDAR Point Clouds
Abstract
:1. Introduction
2. Materials and Methods
2.1. MLS Datasets
2.2. Computation of MLS Point Cloud Attributes
2.3. Filtering Method
2.3.1. Grid Construction
- Finding the minimum angular position and the minimum longitudinal distance .
- Determining the angular resolution and distance resolution .
- Assigning the MLS point to the grid cell with row index and column index computed using Equations (3) and (4). If more than one point is assigned to the same grid index, the point with largest range attribute is preserved.
2.3.2. Single Cross-Section Filtering (SCSF)
2.3.3. Multiple Cross-Section Filtering (MCSF)
3. Results
4. Discussion
4.1. Accuracy
4.2. Parameter Setting
4.3. THE Combination of Multiple Filtering Methods
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Dataset | Length (m) | Number of Points (million) | Ground Surface Type | Typical Non-Ground Objects |
---|---|---|---|---|
Subset 1 | 295 | 14.2 | Flat Gentle | Large numbers of trees Large numbers of cars Lamps, buildings |
Subset 2 | 140 | 4.7 | Flat Steep | Large numbers of trees Large numbers of cars Power lines |
Subset 3 | 290 | 9.3 | Flat Gentle | Large number of trees Cars, power lines |
Subset 4 | 270 | 9.6 | Flat Gentle | Large numbers of trees Cars, buildings |
Subset 5 | 320 | 9.6 | Flat Steep | Trees, power lines Cars, overpass road |
Dataset | Type I Error | Type II Error | Total Error | ||||||
---|---|---|---|---|---|---|---|---|---|
CSF | PMF | Proposed Method | CSF | PMF | Proposed Method | CSF | PMF | Proposed Method | |
Subset 1 | 7.745% | 0.872% | 0.790% | 1.800% | 2.172% | 0.830% | 5.939% | 1.267% | 0.802% |
Subset 2 | 12.077% | 3.318% | 1.872% | 1.133% | 2.811% | 3.741% | 7.160% | 3.090% | 2.712% |
Subset 3 | 3.252% | 1.270% | 1.470% | 2.108% | 2.699% | 1.382% | 2.801% | 1.833% | 1.435% |
Subset 4 | 1.996% | 0.470% | 1.253% | 2.104% | 3.300% | 1.266% | 2.027% | 1.283% | 1.256% |
Subset 5 | 14.606% | 2.342% | 1.743% | 1.831% | 2.566% | 2.204% | 10.155% | 2.420% | 1.904% |
Average | 7.935% | 1.654% | 1.426% | 1.795% | 2.710% | 1.885% | 5.616% | 1.979% | 1.622% |
Dataset | Attribute Computation (s) | Filtering (s) | Total (s) |
---|---|---|---|
Subset 1 | 1308.5 | 227.5 | 1536.0 |
Subset 2 | 278.0 | 30.7 | 308.7 |
Subset 3 | 729.3 | 95.1 | 824.4 |
Subset 4 | 882.9 | 106.6 | 989.5 |
Subset 5 | 817.1 | 145.4 | 962.5 |
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Yan, L.; Liu, H.; Tan, J.; Li, Z.; Chen, C. A Multi-Constraint Combined Method for Ground Surface Point Filtering from Mobile LiDAR Point Clouds. Remote Sens. 2017, 9, 958. https://doi.org/10.3390/rs9090958
Yan L, Liu H, Tan J, Li Z, Chen C. A Multi-Constraint Combined Method for Ground Surface Point Filtering from Mobile LiDAR Point Clouds. Remote Sensing. 2017; 9(9):958. https://doi.org/10.3390/rs9090958
Chicago/Turabian StyleYan, Li, Hua Liu, Junxiang Tan, Zan Li, and Changjun Chen. 2017. "A Multi-Constraint Combined Method for Ground Surface Point Filtering from Mobile LiDAR Point Clouds" Remote Sensing 9, no. 9: 958. https://doi.org/10.3390/rs9090958