1. UAV LiDAR

2. Reasons to UAV LiDAR Easy mobilization Low operation costs
Through mass production of sensors & GNNS available to manufacture even in small companies
Data quality compared to ‘professional’ systems not as high but sufficient to many targets
Lot of small projects and new applications like supplementary mapping, project monitoring etc.
Compared to photogrammetric point clouds can see ground also on covered areas
Special targets like power lines
Lot of potential new users

3. Mapping categories in SWOT
Coverage
Small
Field
UAV
Aerial Remote Sensing
LiDAR
Photogrammetry
High
Flexibility
Low
Satellite
Large

4. Limits of UAV LiDAR Reliability : ’all UAVs crush in one day’
Operation time 15…30 minutes => small areas, short corridors
Permissions:
< 150 m altitude
< 500 m from the pilot
Visible during operation
Depends on national laws/ rules

5. System Concept Needed software: Survey planning Navigation
GPS antenna
SPAN integrates GNSS with Inertial Measurement Unit (IMU) => Exact position and orientation of system continuously
Needed software:
Survey planning
Navigation
Post-processing
Data calibration and classification
Image processing
Board + memory for navigation and data saving
Scanner with 32 channels
Camera

7. LiDAR sensors
Velodyne announced yesterday that the company is set to begin high-volume manufacturing of sensors. Enabled by strong sales of the VLP-16 “Puck” LiDAR sensors, the company has invested in a production-line style manufacturing process for existing and forthcoming products, including a new automotive sensor that is currently in the preliminary design concept phase. 
Lower Costs for All Velodyne Sensors First, Juchmann explained that Velodyne is scaling up their production to allow the company to meet the price demands of the automotive industry.
The automotive market requires sensors that cost even less than the VLP-16, he said. “The big driver to get the cost further down, that’s the automotive industry. Obviously, if you buy a car and you have to spend $8,000 on an additional sensor, that just doesn’t work. The car industry is looking for below $1,000, even well below $500 for a sensor.” 

9. Principle of orientation/ location
IMU + GNSS
Defines the location and orientation of the system
XYZ:
WGS84 geocentric
Camera
Distance
RTK GNSS Receiver
PostProcessing

10. Data Accuracy Error Sources: Other
Incorrect trajectory solution (poor GNNS)
No or poor data calibration
Final matching to control points missing
Insufficient data classification
Other
Co-ordinate transformation
Matching to poor Geoid surface

11. Position Improvement by Post-processing
Flight line
= GPS + IMU (original)
= GPS + IMU + RTK
= post-processing
= GNSS position
= rejected GNSS position

12. Data Calibration/ TerraMatch
real
theoretical
Heading
Flight direction
Roll
Pitch
Solution:
To compare overlapping point clouds to find correction parameters
Surface comparison
Tie line comparison
Before
After

14. Target Co-ordinate System
Planar XY
In general bases on Gauss-Kruger etc.
Mathematical form between east/north co-ordinates and longitude/ latitude
Elevation
Bases on geoid
Zero elevation medium sea water level
No mathematical form

15. Nature of Laser Point & Accuracy
Laser pulse expands with increasing distance: Divergense 3 mrad => footprint 15 cm from 50 m altitude (Velodyne VLP-16)
Laser points are not points
In quality control one compares the distance from ground surface (created from laser point) to control points. Not the accuracy of single laser point.

16. Phoenix Aerial ALS32-LiDAR Data Processing by Terrasolid software
Goals:
To test the calibration and data quality of Phoenix Aerial ALS32 system by Terrasolid software
Operator:Phoenix Aerial Systems
Date:
System: Phoenix Aerial ALS32 with Sony Alpha A6000 camera + Velodyne 32 scanner
Altitude: 30-35m
Data set:
- Trajectories, 200 Hz
- Total points
- Pictures tota 249

17. Data Processing Overview
Software
TerraScan – for reading-in datasets in to geographical blocks
TerraMatch- Data calibration
TerraPhoto – Image processing
In calibration only a limited area used
Continuous trajectory was cut and the pieces were numbered
All returning curves were removed
Some hard surfaces and buildings on the ares

18. Calibration and quality improvements
Hard surface classification => removes all except the medium level point from total
Measure Match to get a value of the difference of hard surfaces from different trajectories
Noise before data calibration
Coloring by 32 LiDAR channels of the HDL-32E scanner.
Coloring by trajectories
Noise before calibration:
Trajectory Points Magnitude Dz

19. Calibration by ‘tielines’
To correct heading, pitch, roll and dz of the system
TerraMatch fit lines along points from overlapping trajectories.
Compares the differences to find correction parameters
Applies the corrections to the whole data set
Point coloring after corrections
Coloring by 32 LiDAR channels of the HDL-32E scanner.
Coloring by trajectories
Noise after calibration:
Trajectory Points Magnitude Dz

20. dz fluctuation corrections
Last step
Define the dz fluctuation by time
Apply the corrections to the whole data set
Point coloring after corrections
Coloring by 32 LiDAR channels of the HDL-32E scanner.
Coloring by trajectories
Noise after correction
Trajectory Points Magnitude Dz

21. Smoothen the points
An iterative process, which uses local small surface area to lift and lower the points to improve the surface geometry
Point coloring after corrections
Coloring by 32 LiDAR channels of the HDL-32E scanner.
Coloring by trajectories
Noise after smoothening:
Trajectory Points Magnitude Dz

22. System Analyses Scanning pattern is wide and point density high.
Suits well for mobile mapping where distances are short.
Due to the scanning pattern each target produces several echoes, of which a part is perpetual, a part oblique => a part of the total noise.
Oblique hits cause a part of the noise ; limit max 30 m distance.
Fan-like Scanning pattern in airborne LiDAR
Scan angle left and right
Scan angle along trajectory

23. Conclusions
In flight planning sufficient overlap to get enough perpetual hits for calibration.
Scan angel < +/-20 degrees
Hard surfaces and building roofs needed for calibration
With careful calibration one could reach in maximum 2 cm accuracy