LiDAR Calibration and Validation Software and Processes

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LiDAR Calibration and Validation Software and
Processes
http//dprg.geomatics.ucalgary.ca Department of
Geomatics Engineering University of Calgary,
Canada
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Acknowledgement
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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

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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 procedure.
• Quality control (Post-mission)
• Provide routines and consistent checks to ensure
  data integrity, correctness, and completeness
• Check whether the desired quality has been
  achieved

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LiDAR QA

• 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

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LiDAR QA System Calibration
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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

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LiDAR QA System Calibration

• 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

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LiDAR QA System Calibration
Error sources analysis / Error Modeling
Primitives
Recoverability analysis
Aspects Involved
Correspondence
Flight configuration
Sampling Density
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LiDAR QA System Calibration

• Calibration/system parameters
• Spatial and rotational offsets between various
  system components (?X, ?Y, ?Z, ??, ??, ??)
• Range bias (??)
• 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.

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LiDAR QC

• 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.

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LiDAR QA/QC DPRG Approach

• 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
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LiDAR QA/QC DPRG Approach
Simplified Calibration

• LiDAR Data in Overlapping Parallel Strips
• Point cloud coordinates
• Raw measurements are not necessarily available

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LiDAR QA/QC DPRG Approach
Simplified Calibration

• LiDAR Data in Overlapping Parallel Strips
• Point cloud coordinates
• Raw measurements are not necessarily available

Overlapping strips
Discrepancies
3D Transformation
Rotation
Calibration Parameters
Shifts
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LiDAR QA/QC DPRG Approach
Simplified Calibration
Local coordinate system
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LiDAR QA/QC DPRG Approach
Simplified Calibration
Overlapping strips
Discrepancies
Rigid body Transformation Three translations and
a roll angle
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LiDAR QA/QC DPRG Approach
Simplified Calibration
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LiDAR QA/QC DPRG Approach
Simplified Calibration
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LiDAR QA/QC DPRG Approach
Quasi-Rigorous Calibration
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LiDAR QA/QC DPRG Approach
Quasi-Rigorous Calibration

• LiDAR Data in Overlapping Strips
• Point cloud coordinates with the time tag
• Time-tagged trajectory

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LiDAR QA/QC DPRG Approach
Quasi-Rigorous Calibration
Assuming that A and B are conjugate points
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LiDAR QA/QC DPRG Approach
Quasi-Rigorous Calibration
Assuming that A and B are conjugate points
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LiDAR QA/QC DPRG Approach
Optimum Flight Configuration
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LiDAR QA/QC DPRG Approach
Point/Patch Pairs Closest Patch Procedure
Conjugate patch to a given point

• Procedures have been developed to deal with the
  absence of corresponding points within conjugate
  point-patch pairs.

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Evaluation Criteria

• 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

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Experimental Results
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
Data Captured by ALS50
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Experimental Results
Overlapping Strip Pairs Overlap Direction
Strips 12 80 Opposite directions
Strips 34 25 Opposite directions
Strips 45 75 Opposite directions
Strips 56 50 Same direction
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Experimental Results
Estimated biases in the system parameters
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Experimental Results
Impact on Generated Profiles
Original Point Cloud
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Experimental Results
Impact on Generated Profiles
Adjusted Point Cloud
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Experimental Results
Impact on Existing Discrepancies
Before Calibration Before Calibration Before Calibration After Calibration After Calibration After Calibration
Strips 12 Strips 12 Strips 12 Strips 12 Strips 12 Strips 12
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) f (deg) ? (deg) ? (deg) f (deg) ? (deg)
0.0001 -0.052 -0.002 0.0012 -0.0016 -0.0055
Strips 34 Strips 34 Strips 34 Strips 34 Strips 34 Strips 34
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) f (deg) ? (deg) ? (deg) f (deg) ? (deg)
0.0484 -0.0005 -0.0011 0.0075 0.0009 -0.0013
Compatibility between overlapping strips before
and after the calibration procedure
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Experimental Results
Impact on Absolute Accuracy
Photogrammetric Data

• Six flight lines
• Four parallel flight lines _at_ 550m (50 side lap)
• Two opposite flight lines _at_ 1200m (100 side lap)

Camera Specifications
Camera model Rollei P-65
Array dimension 8984x6732 pixels
Pixel size 6µm
Nominal focal length 60mm
Camera classification Normal Angle Camera (AFOV 58.6º)
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Experimental Results
Impact on Absolute Accuracy
  Before Calibration After Calibration
Mean ?X (m) -0.03 0.02
Mean ?Y (m) -0.18 0.01
Mean ?Z (m) 0.15 0.08
sX (m) 0.11 0.05
sY (m) 0.15 0.06
sZ (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
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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.

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• Comments and Questions?