Flight Planning for LiDAR-Based UAS Mapping Applications
Abstract
:1. Introduction
- (1)
- How to design a flight plan for mapping an area using a low-cost multi-beam LiDAR mounted on a UAS platform and what are the input and output parameters?
- (2)
- What is the expected point density in an urban region at different flying heights, sidelaps, and flying speeds using low-cost multi-beam LiDARs?
- (3)
- Among the considered low-cost LiDAR sensors, which one is more efficient for mapping purposes in terms of coverage and point density?
2. Methodology and Developed Tool
- Swath width of the scan
- Number of flight strips
- Separation distance between flight strips
- Number and location of the flight waypoints
- Estimated point density
- Estimated flight duration
2.1. Flight Plan Design
- LiDAR specifications: Every multi-beam LiDAR sensor has its own geometric structure, which includes FOV, angular distribution of beams, angular resolution, output rate pts/m2, rotation speed, and the maximum scanning range. Table 1 shows the characteristics of the three selected low-cost multi-beam LiDAR sensors, as published by their manufacturers [35,36,37].
- Flight plan specifications: They include flying height H, flying speed m/s, and the required sidelap % between adjacent scanning strips. It should be noted that a larger overlap percentage ensures higher coverage but requires a longer flight time, which should be carefully considered.
- : project area dimensions defined as a rectangle with a length and width .
- flying height above the ground level.
- scanning range of the LiDAR.
- distance between two successive waypoints.
- : along track scanning width.
- : across track swath width of the scanning.
- : scanning field of view out of the offered 360° FOV.
- : separation distance between the flight strips.
- : number of flight strips rounded to positive infinity.
- number of waypoints per strip.
2.2. Scanning Simulation
- : the measured range distance from the LiDAR to the object points.
- : rotation matrix of the boresight angles.
- : the measured azimuth angle of the laser beam.
- : the vertical angle of the laser beam measured from the horizon.
- , and : the coordinates of the LiDAR sensor.
- and : the coordinates of the scanned point.
- -
- Compute the scan vector from the LiDAR to the object direction point . This vector might intersect the simulated object before or after at (Figure 5).
- -
- Define the object plane normal by the parameters.
- -
- Compute the vector = Dot product), which should be a non-zero value if the LiDAR beam line and the object plane are not parallel and should intersect at a unique object point .
3. Results and Discussion
3.1. Flight Planning Tool
3.2. Estimated Average Point Density
3.3. UAS LiDAR Mapping of an Urban Area
- -
- In contrary to the image-based UAS missions, even with the nadir orientation of the LiDAR, a significant coverage and point density on building facades could be attained.
- -
- M8 had a higher performance than VLP-16 LiDAR and OS-1-16, especially on facade features.
- -
- Density achieved on facades was approximately half the density achieved on the ground and roof features.
- -
- Ouster OS-1-16 slightly outperformed the VLP-16 LiDAR on the ground and facades.
- -
- The density achieved on facades was better at the high altitude parts than the lower altitude parts.
- -
- Sample the reference model as a point cloud with a uniform spacing of 10 cm.
- -
- For every point in the reference point cloud, find any scanning point within a 25 cm search radius.
- -
- Label reference points as “covered” if they have a neighboring scanned point, or “uncovered” if there are distant scanned points (≥25 cm).
- -
- Use the “uncovered” points as an indicator of completeness.
3.4. UAS LiDAR Mapping of a Communication Tower
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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LiDAR Sensor/System | Velodyne [37] | Quanergy [35] | Ouster [36] |
---|---|---|---|
Type/version | VLP-16 | M8 | OS-1-16 |
Max. Range | ≤ 100 m | > 100 m @ 80% | ≤ 120 m @ 80% |
Range Accuracy 1σ | ±3 cm (Typical) | ±3 cm | ±1.5-10 cm |
Output rate pts/sec. | ≈300000 | ≈420000 (1 return) | ≈327680 |
FOV - Vertical | ≈30° (±15°) | ≈20° (+3°/–17°) | ≈33.2° (±16.6°) |
Rotation rate | 5-20 Hz | 5-20 Hz | 10-20 Hz |
Vertical resolution | V:2° | V:3° | V: 2.2° |
Horizontal resolution | H: 0.4°@ 20Hz | H: 0.14°@ 20Hz | H:0.35°@ 20Hz |
Weight | 830 g | 900 g | 425 g |
Power consumption | 8 w | 18 w | 14-20 w |
Price | $8K | $5K | $3.5K |
Speed @ 10 m/s | Speed @ 5 m/s | |||
---|---|---|---|---|
Facades and Trees | Ground and Horizontal Surfaces | Facades and Trees | Ground and Horizontal Surfaces | |
M8 | 81 | 131 | 162 | 260 |
OS1-1-16 | 64 | 105 | 127 | 207 |
VLP-16 | 58 | 98 | 116 | 192 |
LiDAR Type | Density pts/m2 | Lack of Coverage |
---|---|---|
VLP-16 | 162 ± 62 | 11.1 % |
OS-1-16 | 184 ± 70 | 8.2 % |
M8 | 235 ± 93 | 7.6 % |
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Alsadik, B.; Remondino, F. Flight Planning for LiDAR-Based UAS Mapping Applications. ISPRS Int. J. Geo-Inf. 2020, 9, 378. https://doi.org/10.3390/ijgi9060378
Alsadik B, Remondino F. Flight Planning for LiDAR-Based UAS Mapping Applications. ISPRS International Journal of Geo-Information. 2020; 9(6):378. https://doi.org/10.3390/ijgi9060378
Chicago/Turabian StyleAlsadik, Bashar, and Fabio Remondino. 2020. "Flight Planning for LiDAR-Based UAS Mapping Applications" ISPRS International Journal of Geo-Information 9, no. 6: 378. https://doi.org/10.3390/ijgi9060378