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Ayman F. Habib, 2010
LiDAR Calibration and Validation Software and Processes
http://dprg.geomatics.ucalgary.caDepartment of Geomatics Engineering
University of Calgary, Canada1
Ayman F. Habib, 20102
Acknowledgement
McElhanney Consulting McElhanney Consulting Services Ltd., Vancouver, BC, Services Ltd., Vancouver, BC,
CanadaCanada
Ayman F. Habib, 20103
Overview
• LiDAR systems: QA/QC• LiDAR system calibration
– Simplified calibration procedure
– Quasi-rigorous calibration procedure
– Evaluation (QC) criteria• Relative accuracy
• Absolute accuracy
– Experimental results
• Concluding remarks
Ayman F. Habib, 20104
Quality Assurance & Quality Control
• Quality assurance (Pre-mission): – Management activities to ensure that a process, item, or
service is of the quality needed by the user– It deals with creating management controls that cover
planning, implementation, and review of data collection activities.
– Key activity in the quality assurance is the calibration calibration procedureprocedure.
• Quality control (Post-mission):– Provide routines and consistent checks to ensure data
integrity, correctness, and completeness– Check whether the desired quality has been achieved
Ayman F. Habib, 20105
• QA activities/measures include:– Optimum mission time
– Distance to GNSS base station
– Flying height
– Pulse repetition rate
– Beam divergence angle
– Scan angle
– Percentage of overlap
– System calibration
LiDAR QA
Ayman F. Habib, 2010
0
0lulb
blu
mbG
mboG RRRPRXX
6
System Calibration
LiDAR QA: System Calibration
Ayman F. Habib, 20107
LiDAR QA: System Calibration
• The calibration of a LiDAR system aims at the estimation of systematic errors in the system parameters.– One can assume that the derived point cloud after
system calibration is only contaminated by random errors.
• Usually accomplished in several steps:– Laboratory calibration,
– Platform calibration, and
– In-flight calibration
Ayman F. Habib, 20108
• Drawbacks of current in-flight calibration methods:– Some techniques require the raw data, which is not always
available.
– Time consuming and expensive
– Generally based on complicated and sequential calibration procedures
– Require some effort in ground surveying of the control points/surfaces
– Some of these calibration procedures involve manual and empirical procedures.
– Lack of a commonly accepted methodology
LiDAR QA: System Calibration
Ayman F. Habib, 20109
Error sources Error sources analysis / analysis /
Error Error ModelingModeling
Recoverability Recoverability analysisanalysis
Flight Flight configurationconfiguration
Sampling Sampling DensityDensity
CorrespondenceCorrespondence
PrimitivesPrimitives
Aspects Aspects InvolvedInvolved
LiDAR QA: System Calibration
Ayman F. Habib, 201010
• Calibration/system parameters:– Spatial and rotational offsets between various system
components (ΔX, ΔY, ΔZ, Δ, Δ, Δ)
– Range bias (Δ)
– Mirror angle scale (S)
• The system parameters can be estimated using the original LiDAR equation (rigorous approach).– Raw measurements should be available.
• These parameters can be estimated using a simplified version of the LiDAR equation (approximate approach).– Raw measurements need not be available.
LiDAR QA: System Calibration
Ayman F. Habib, 201011
• Quality control is a post-mission procedure to ensure/verify the quality of collected data.
• Quality control procedures can be divided into two main categories:– External/absolute QC measures: the LiDAR point cloud
is compared with an independently collected surface.• Check point analysis
– Internal/relative QC measures: the LiDAR point cloud from different flight lines is compared with each other to ensure data coherence, integrity, and correctness.
LiDAR QC
Ayman F. Habib, 201012
• LiDAR data is usually acquired from parallel flight lines with some overlap between the collected data.
• DPRG Concept: Evaluate the degree of consistency among the LiDAR footprints in overlapping strips.
Strip 2 Strip 3 Strip 4
LiDAR QA/QC: DPRG Approach
Ayman F. Habib, 201013
•LiDAR Data in Overlapping Parallel Strips
Point cloud coordinates Raw measurements are not necessarily
available
LiDAR QA/QC: DPRG ApproachSimplified Calibration
Ayman F. Habib, 201014
Overlapping strips
Discrepancies
3D Transformation
Rotation
ShiftsCalibration Parameters
•LiDAR Data in Overlapping Parallel Strips
Point cloud coordinates Raw measurements are not necessarily
available
QC ProcedureQA Procedure
LiDAR QA/QC: DPRG ApproachSimplified Calibration
Ayman F. Habib, 2010
YM
ZM
XM
Zlu
Ylu
Xlu
Zlu
Ylu
Xlu
Mapping Frame
Laser Unit Frame
Laser Unit Frame
PZ
Y
X
User Defined Coordinate
System
15
LiDAR QA/QC: DPRG ApproachSimplified Calibration
Local coordinate system
Ayman F. Habib, 201016
},,,,,,{},,,{ SYXrollZYX TTT
Overlapping strips
Discrepancies
Rigid body Transformation: Three translations and a roll angle
LiDAR QA/QC: DPRG ApproachSimplified Calibration
Ayman F. Habib, 2010
Dx =
0
Zlu
Ylu
Xlu
Zlu
Ylu
Xlu
Laser Unit Frame
Laser Unit Frame
Strip A
Strip B
Z
Y
X
User Defined Coordinate
System
Backward
Forward
xA =
-xB
P
'2
'
0
22
22
),,,(
BG
AG XRHY
HX
X
andYXf
LiDAR QA/QC: DPRG ApproachSimplified Calibration
17
Ayman F. Habib, 2010
LiDAR QA/QC: DPRG ApproachSimplified Calibration
Dx
Zlu
Ylu
Xlu
xA
xB
Laser Unit Frame
PStrip AStrip B
Z
Y
X
User Defined Coordinate
System
Forward
Zlu
Ylu
Xlu
Laser Unit Frame
Forward
'2
'
)/(),,,(
BG
SH
D
X
X
XXAG XR
DD
SDHDX
andSf
X
18
Ayman F. Habib, 2010
LiDAR QA/QC: DPRG ApproachQuasi-Rigorous Calibration
19
Ayman F. Habib, 2010
•LiDAR Data in Overlapping Strips Point cloud coordinates with the time tag Time-tagged trajectory
LiDAR QA/QC: DPRG ApproachQuasi-Rigorous Calibration
Trajectory point
Estimated trajectory
point
Fitted trajectory line
Time-tagged laser pointx(t)
z(t)
N
κ(t)
β(t)
P( t)
ti ti+1
ti+2
ti+3
20
Ayman F. Habib, 201021
AB
BBAA
BBBAAA
BBBAAA
BA
BBAA
BBAA
BBAA
BBAA
BA
BBAABBAA
BBAABBAA
BABA
BABA
BiasedB
BiasedA
BiasedB
BiasedA
BiasedB
BiasedA
e
Sxx
Szz
Szz
SS
SS
SS
xx
xx
xx
zzzz
zzzz
YX
YX
ZZ
YY
XX
)(
sinsin
coscos
coscos
sinsinsinsin
sincossincos
0
coscos
sinsin
)(
sinsincoscos
coscossinsin
0
coscossinsin
sinsincoscos
~~
~~
~~
Assuming that A and B are conjugate points
LiDAR QA/QC: DPRG ApproachQuasi-Rigorous Calibration
Ayman F. Habib, 201022
AB
BB
BBB
BBB
B
BB
BB
BB
BB
B
BBBB
BBBB
BB
BB
BiasedB
ControlA
BiasedB
ControlA
BiasedB
ControlA
e
Sx
Sz
Sz
S
S
S
x
x
x
zz
zz
Z
YX
YX
ZZ
YY
XX
sin
cos
cos
sinsin
sincos
0
cos
sin
sin cos
cos sin
cos sin
sin cos
~~
~~
~~
Assuming that A and B are conjugate points
LiDAR QA/QC: DPRG ApproachQuasi-Rigorous Calibration
Ayman F. Habib, 201023
Optimum Flight Configuration
LiDAR QA/QC: DPRG Approach
Ayman F. Habib, 201024
Conjugate patch to a given point
Point/Patch Pairs: Closest Patch Procedure
Conditions:• Closest patch (within
a threshold)• Point located within
the patch
LiDAR QA/QC: DPRG Approach
• Procedures have been developed to deal with the absence of corresponding points within conjugate point-patch pairs.
Ayman F. Habib, 2010
• Relative Accuracy– Qualitative Evaluation:
• Intensity images before and after the point cloud adjustment• Profiles before and after the point cloud adjustment• Segmented point cloud
– Quantitative Evaluation:• Average noise level within segmented point cloud• Discrepancies between overlapping strips before and after the point
cloud adjustment
• Absolute Accuracy– LiDAR features, derived from the original and adjusted point
cloud, are used for photogrammetric geo-referencing– Check point analysis
25
Evaluation Criteria
Ayman F. Habib, 201026
Strip Number
Flying Height
Direction
1 1150 m N-S
2 1150 m S-N
3 539 m E-W
4 539 m W-E
5 539 m E-W
6 539 m E-W
Strip 1
Strip 2
Strip 3
Strip 4
Strip 5
Strip 6
Strip 1
Strip 2
Strip 3
Strip 4
Strip 5
Strip 6
Experimental Results
Data Captured by ALS50
Ayman F. Habib, 201027
OverlappingStrip Pairs
Overlap % Direction
Strips 1&2 80% Opposite directions
Strips 3&4 25% Opposite directions
Strips 4&5 75% Opposite directions
Strips 5&6 50% Same direction
Experimental Results
Ayman F. Habib, 201028
Experimental Results
Estimated biases in the system parameters
Ayman F. Habib, 201029
Experimental Results
Original Point Cloud
1m1m
Impact on Generated Profiles
Ayman F. Habib, 201030
Experimental Results
Adjusted Point Cloud
1m1m
Impact on Generated Profiles
Ayman F. Habib, 2010
Before Calibration After Calibration
Strips 1&2 Strips 1&2
XT (m) YT (m) ZT (m) XT (m) YT (m) ZT (m)
1.10 -0.32 -0.01 0.10 0.07 -0.05
(deg) φ (deg) κ (deg) (deg) φ (deg) κ (deg)
0.0001 -0.052 -0.002 0.0012 -0.0016 -0.0055
Strips 3&4 Strips 3&4
XT (m) YT (m) ZT (m) XT (m) YT (m) ZT (m)
0.18 0.41 -0.01 -0.01 0.03 0.00
(deg) φ (deg) κ (deg) (deg) φ (deg) κ (deg)
0.0484 -0.0005 -0.0011 0.0075 0.0009 -0.0013
Compatibility between overlapping strips before and after the calibration procedure
31
Experimental ResultsImpact on Existing Discrepancies
Ayman F. Habib, 2010
Photogrammetric Data
Camera model Rollei P-65
Array dimension 8984x6732 pixels
Pixel size 6μm
Nominal focal length 60mm
Camera classificationNormal Angle Camera (AFOV =
58.6º)
Camera Specifications
• Six flight lines:• Four parallel flight lines @ 550m (50%
side lap)• Two opposite flight lines @ 1200m
(100% side lap)
L1 L1
L2 L2
L3L3
L4L4
L5L6
L5L6
Impact on Absolute AccuracyExperimental Results
Ayman F. Habib, 201033
Before Calibration After Calibration
Mean ΔX (m) -0.03 0.02
Mean ΔY (m) -0.18 0.01
Mean ΔZ (m) 0.15 0.08
σX (m) 0.11 0.05
σY (m) 0.15 0.06
σZ (m) 0.17 0.18
RMSEX (m) 0.11 0.06
RMSEY (m) 0.23 0.06
RMSEZ (m) 0.23 0.19
RMSETOTAL (m) 0.34 0.21
RMSE analysis of the photogrammetric check points using extracted control linear features from the LiDAR data before and after the
calibration procedure
Experimental ResultsImpact on Absolute Accuracy
Ayman F. Habib, 201034
Concluding Remarks• In spite of the technical advances in LiDAR
technology, there is still a lack of well defined procedures for the Quality Assurance (QA) and Quality Control (QC) of the Mapping process.
• These procedures should be capable of the dealing with the nature/restrictions of the current mapping procedure.– Absence of the system raw measurements– Challenge in having LiDAR specific control targets
• This research has developed a calibration procedure that led to improvements in the relative and absolute accuracy of the adjusted point cloud.
Ayman F. Habib, 201035
Comments and Questions?