A Hovercraft-Borne LiDAR and a Comprehensive Filtering Method for the Topographic Survey of Mudflats
<p>Hovercraft-borne laser scanning system. GNSS: Global Navigation Satellite System; IMU: inertial measurement unit; POS: position and orientation system.</p> "> Figure 2
<p>The model of point coordinate calculation.</p> "> Figure 3
<p>Overview of (<b>a</b>) the cloth simulation algorithm and (<b>b</b>) the fitting ground surface.</p> "> Figure 4
<p>Segmentation-based filtering.</p> "> Figure 5
<p>The process of the comprehensive point cloud filtering.</p> "> Figure 6
<p>(<b>a</b>) The test mudflat zone (<b>b</b>) during high tide, (<b>c</b>) during low tide. (<b>d</b>) Lifting the equipment; (<b>e</b>) Topographic survey.</p> "> Figure 7
<p>Point clouds of the area: (<b>a</b>) 3-D point cloud; (<b>b</b>) Intensity point cloud; (<b>c</b>) The DSM (digital surface model) and Real-Time Kinematic (RTK) points (Red points).</p> "> Figure 8
<p>The point cloud segmentation results. Black denotes the background, which means there were no points in these areas. There were about 130,000 segments after segmentation, and each segment is shown with one color.</p> "> Figure 9
<p>The point cloud results obtained by the three filtering methods: (<b>a</b>) by CSF; (<b>b</b>) by angle-constrained CSF; and (<b>c</b>) by comprehensive filtering.</p> "> Figure 10
<p>The DTM (digital terrain model) results of the three filtering methods: (<b>a</b>) by CSF; (<b>b</b>) by angle-constrained CSF; and (<b>c</b>) by comprehensive filtering.</p> "> Figure 11
<p>The distribution of elevation deviations between RTK points and laser points.</p> "> Figure 12
<p>Blank areas: (<b>a</b>) formed due to the shield of the hovercraft; (<b>b</b>) produced because of the specular reflection of water.</p> "> Figure 13
<p>An example of low outliers.</p> "> Figure 14
<p>Comparison of the three segmentation methods: (<b>a</b>) based only on normal vector; (<b>b</b>) based only on intensity; (<b>c</b>) based on the combination of normal vector and intensity.</p> ">
Abstract
:1. Introduction
2. Hovercraft-Borne LiDAR System
3. Ground Points Acquisition
3.1. Data Synchronization
3.2. Coordinate Calculation of Laser Points
3.3. Comprehensive Filtering of the Point Cloud
3.3.1. Point Cloud Segmentation by Combining Normal Vector and Intensity
- For each point, its normal vector and curvature are estimated using the points within radius r to fit a local surface. The unitized normal vector and normalized intensity value are used to build a four-dimensional attribute vector A:A = [ Nx Ny Nz I ],
- Among all points, the point with the minimum curvature is selected as a seed point P0, and a new segment O is generated. P0 is included in O and marked as a segmented point.
- The points around P0 within the radius R are searched as candidate points. For each candidate point Pi, the distance between the attribute vectors of Pi and P0 is calculated by Equation (5):
- The threshold d is set in the region growing. Whether or not the candidate point is in O is judged based on the following principle:
- Repeating (4), all the seed points around P0 within the radius R are found and marked as segmented points.
- By region growing, the new seed points are further found by repeating (3)–(5) and are marked as segmented points until no new point is found and marked. These seed points are all included in O. Then the segment O is segmented completely.
- In the remaining unsegmented points, we can also find the point with the minimum curvature. By following (2)–(6), a new segment is generated. Similarly, we can obtain other segments by repeating (2)–(6) until all the points are segmented.
3.3.2. Fitting the Ground Surface and Calculating Its Normal Vector
3.3.3. Segmentation-Based Point Cloud Filtering
- For each segment, the points belonging to the segment are diagnosed as possible ground points by the following criteria:
- •
- The distance from a diagnosed point to the fitting surface is less than the given threshold h.
- •
- The angle between the normal vectors of the point and the fitting surface is less than the given threshold θ.
- For each segment, the proportion of the possible ground points in the total points of the segment is calculated. If the proportion is higher than the given threshold p, the segment is judged as a ground segment, and all the points belonging to the segment are judged as ground points; otherwise, they are judged as non-ground points.
3.3.4. The Comprehensive Point Cloud Filtering Process
3.4. Accuracy Assessment
4. Experiment and Analysis
4.1. Overview of the Experiment
4.2. Data Processing and Analysis
5. Discussion
5.1. The Mobile Measuring System
5.2. Obtaining the Mudflat Topography
5.2.1. Removal of Outliers Lower than the Mudflat Surface before Filtering
5.2.2. Point Cloud Segmentation by Combining Normal Vector and Intensity
5.2.3. Filtering Parameter Setting
5.2.4. Interpolation in Blank Areas
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Equipment | Specification | Accuracy/Range | Specification | Accuracy/Range |
---|---|---|---|---|
R-Angle 0300 Laser Scanner | Min. Range (m) | 1.5 | Field of View (°) | 360 |
Max. Range (m) | 600 | Echoes | Multiple | |
Max. Pulse Frequency (kHz) | 1000 | Wavelength (nm) | 1550 | |
Scan Speed (Hz) | 10–200 | Scanner Weight (kg) | <5 | |
Beam Divergence (mrad) | <0.35 | Power Supply (V DC) | 18–32 | |
Angle Resolution (°) | 0.001 | Power Consumption (W) | <100 | |
Angle Precision (°) | 0.005 | Storage Temperature (℃) | −35 to 70 | |
Range Precision (mm @ 100 m) | 5–8 | Operation Temperature (℃) | −20 to 65 | |
APPLANIX POS AV610 | GNSS Position (m) | 0.05–0.3 | Roll and Pitch (°) | 0.0025 |
Velocity (m/s) | 0.005 | True Heading (°) | 0.005 | |
CH-4 Hovercraft | Size (m) | 4.6 × 2.4 × 1.65 | Hover Height (mm) | ≤220 |
Weight (kg) | 380 ± 50 | Speed (mud/sand) (km/h) | 30–45 |
Method | Point Cloud Segmentation | Object-Based Filtering | ||||
---|---|---|---|---|---|---|
r (m) | R (m) | d | h (m) | θ (°) | p (%) | |
Traditional CSF | - | - | - | 0.1 | - | - |
Angle-constrained CSF | - | - | - | 0.1 | 30 | - |
Comprehensive filtering | 0.5 | 0.5 | 0.6 | 0.1 | 30 | 75 |
Method | E.I (%) | E.II (%) | E.T (%) |
---|---|---|---|
Traditional CSF | 0.2 | 20.1 | 1.9 |
Angle-constrained CSF | 0.3 | 10.5 | 1.3 |
Comprehensive filtering | 0.2 | 2.8 | 0.3 |
Max. (cm) | Min. (cm) | Average (cm) | RMSE (cm) |
---|---|---|---|
23.0 | −15.3 | −2.1 | 6.4 |
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Zhao, J.; Chen, M.; Zhang, H.; Zheng, G. A Hovercraft-Borne LiDAR and a Comprehensive Filtering Method for the Topographic Survey of Mudflats. Remote Sens. 2019, 11, 1646. https://doi.org/10.3390/rs11141646
Zhao J, Chen M, Zhang H, Zheng G. A Hovercraft-Borne LiDAR and a Comprehensive Filtering Method for the Topographic Survey of Mudflats. Remote Sensing. 2019; 11(14):1646. https://doi.org/10.3390/rs11141646
Chicago/Turabian StyleZhao, Jianhu, Mingyi Chen, Hongmei Zhang, and Gen Zheng. 2019. "A Hovercraft-Borne LiDAR and a Comprehensive Filtering Method for the Topographic Survey of Mudflats" Remote Sensing 11, no. 14: 1646. https://doi.org/10.3390/rs11141646