Airborne LIDAR Mapping Technology
CRSS/ASPRS 2007 Specialty Conference
October 31, 2007
Flight Planning
Flight Control
Position and Attitude System
Sensor Mount
ALS50
ALS CM
ADS SH52
RCD105
ADS SH51
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LIDAR workflowsfor DEM data production
Leica focus
Sensor performance and data acquisition productivity
GNSS/IMU workflow
GrafNav
IPAS Pro
Point cloud generation
ALS Post Processor
Third party developer focus
Editing and project management productivity
Filtering/editing/QA:
TerraScan/TerraModeler/TerraMatch
VLS LIDAR Analyst
QCoherent LP360
Merrick MARS
Tiltan TLiD
Applied Imagery Quick Time Modeler / Quick Time Reader
LIDAR project management
GeoCUE
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LIDAR workflowshigh speed point cloud generation
Processing Activity Remarks
Processing Time per Flight Hour @
150 kHz PRF
Time (sec) Ratio
IPAS GNSS/IMU Processing - Extraction
Extraction of files from mission drive; 1 hour 20 minutes IPAS "ON" time consisting of 1 hour airborne plus 10 minutes static occupation at beginning and end of flight
12 0.003
IPAS GNSS/IMU Processing - DGNSS Proc. using GrafNav
Formatting data from base station and airborne IPAS GNSS into GrafNav format
180 0.050
IPAS GNSS/IMU Processing - GNSS/IMU Proc. (IPAS Pro)
Integration of processed DGNSS position data and IMU data
68 0.019
IPAS GNSS/IMU Processing - Data Review
Checking position plots and forward/reverse difference plots for proper processing and accuracy
300 0.083
Subtotal - GNSS/IMU Processing 560 0.156
Point Cloud GenerationAssumes 150 kHz laser pulse rate for one hour "on-line" time and 7.8% multiple returns (i.e., average 162 kHz return rate)
4204 1.168
Subtotal - Expected average processing time for 1 flight hour (raw data to point cloud) 4764 1.323
Note: based on processing using workstation equipped with Intel Xeon 5150 @ 2.66 GHz, 3 GB RAM
4
Sample LIDAR-derived DEM data productssafety
Disaster prevention / disaster monitoring
Forest fire fuels assessment
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Sample LIDAR-derived DEM data productssecurity
Defense
Supply route monitoring
Spot reconnaissance
Base mapping
Homeland security
Border monitoring
Urban event risk assessment
Law enforcement
Covert activity detection
6
Sample LIDAR-derived DEM data productsenvironment
Coastal survey
Watershed management
Flood zones
Erosion
Forest management
Tree health
Biometric data
Forest inventory
Development impact / change detection
Image courtesy of Watershed Sciences
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Sample LIDAR-derived DEM data productsfused data for multiple applications
Typical sensors co-collecting with ALS DEM data
Medium-format RGB
Medium-format CIR
Thermal imagery
Hyperspectral imagery
Auxiliary sensors collect:
Additional spectral regions
High definition planimetric data
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Future development in LIDAR-derived DEM workflowsmarket requirements, paths for 3rd-party developers
Speed – but can be overcome with more CPUs
Black box – minimizing human interaction, especially during the filtering and editing stages; possible impact of Full Waveform Digitizing (FWD) LIDAR data on accuracy and ability to filter data
Multi-sensor automation - easier fusion from dissimilar sensors – airborne LIDAR + terrestrial LIDAR, LIDAR + airborne (Vis or NIR) imager, LIDAR + thermal imagery
Auto QC – automating the quantitative measurement of output data quality
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What’s newadditional milestones since ASPRS annual meeting
Huge projects being undertaken w/ MPiA systems (Example – NWG has collected 315,000 km², 835 aircraft hours, 1 point / m², 0 sensor problems to date on 750,000 km² collection)
Number of new system deliveries/demos for high altitude use @ 4500 m – 6000 m AGL
Hexagon acquires NovAtel
Participation in large-scale defense exercises
Customer support staff increased to 30 staff
4500 m AGL, MPiA, 66º FOV, 1.5 m avg. post spcng
Image courtesy of North West Geomatics
Thank you!visit us in our booth
Doug Flint
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