LiDAR Calibration and Validation Software and Processes

1. LiDAR Calibration and Validation Software and Processes http://dprg.geomatics.ucalgary.ca Department of Geomatics Engineering University of Calgary, Canada 1

2. Acknowledgement McElhanney Consulting Services Ltd., Vancouver, BC, Canada 2

3. 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

4. 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 4

5. 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 5

6. System Calibration LiDAR QA: System Calibration 6

7. 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 LiDAR QA: System Calibration 7

8. 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 8

9. LiDAR QA: System Calibration Error sources analysis / Error Modeling Primitives Recoverability analysis Aspects Involved Correspondence Flight configuration Sampling Density 9

10. 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 10

11. 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 11

12. 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. LiDAR QA/QC: DPRG Approach Strip 2 Strip 3 Strip 4 12

13. LiDAR QA/QC: DPRG Approach Simplified Calibration • LiDAR Data in Overlapping Parallel Strips • Point cloud coordinates • Raw measurements are not necessarily available 13

14. QA Procedure QC Procedure 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 14

15. LiDAR QA/QC: DPRG Approach Simplified Calibration Local coordinate system 15

16. LiDAR QA/QC: DPRG Approach Simplified Calibration Overlapping strips Discrepancies Rigid body Transformation: Three translations and a roll angle 16

17. LiDAR QA/QC: DPRG Approach Simplified Calibration 17

18. LiDAR QA/QC: DPRG Approach Simplified Calibration 18

19. LiDAR QA/QC: DPRG Approach Quasi-Rigorous Calibration 19

20. LiDAR QA/QC: DPRG Approach Quasi-Rigorous Calibration • LiDAR Data in Overlapping Strips • Point cloud coordinates with the time tag • Time-tagged trajectory 20

21. LiDAR QA/QC: DPRG Approach Quasi-Rigorous Calibration Assuming that A and B are conjugate points 21

22. LiDAR QA/QC: DPRG Approach Quasi-Rigorous Calibration Assuming that A and B are conjugate points 22

23. LiDAR QA/QC: DPRG Approach Optimum Flight Configuration 23

24. Conditions: • Closest patch (within a threshold) • Point located within the patch 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.

25. 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

26. Experimental Results Data Captured by ALS50

27. Experimental Results

28. Experimental Results Estimated biases in the system parameters

29. Experimental Results Impact on Generated Profiles Original Point Cloud

30. Experimental Results Impact on Generated Profiles Adjusted Point Cloud

31. Experimental Results Impact on Existing Discrepancies Compatibility between overlapping strips before and after the calibration procedure 31

32. Experimental Results Impact on Absolute Accuracy Photogrammetric Data • Six flight lines: • Four parallel flight lines @ 550m (50% side lap) • Two opposite flight lines @ 1200m (100% side lap) Camera Specifications

33. Experimental Results Impact on Absolute Accuracy RMSE analysis of the photogrammetric check points using extracted control linear features from the LiDAR data before and after the calibration procedure 33

34. 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. 34

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