Ayman F. Habib, 2010 LiDAR Calibration and Validation Software and Processes Department of Geomatics Engineering University of Calgary, Canada 1

Ayman F. Habib, Acknowledgement McElhanney Consulting Services Ltd., Vancouver, BC, Canada

Ayman F. Habib, 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, 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. calibration procedure –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

Ayman F. Habib, 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, System Calibration LiDAR QA: System Calibration

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

Ayman F. Habib, 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, 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, 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 3Strip 4 LiDAR QA/QC: DPRG Approach

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

Ayman F. Habib, Overlapping strips Discrepancies 3D Transformation Rotation Shifts Calibration Parameters LiDAR Data in Overlapping Parallel Strips Point cloud coordinates Raw measurements are not necessarily available QC Procedure QA Procedure LiDAR QA/QC: DPRG Approach Simplified Calibration

Ayman F. Habib, LiDAR QA/QC: DPRG Approach Simplified Calibration Local coordinate system

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

Ayman F. Habib, 2010 LiDAR QA/QC: DPRG Approach Simplified Calibration 17

Ayman F. Habib, 2010 LiDAR QA/QC: DPRG Approach Simplified Calibration 18

Ayman F. Habib, 2010 LiDAR QA/QC: DPRG Approach Quasi-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 Approach Quasi-Rigorous Calibration 20

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

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

Ayman F. Habib, Optimum Flight Configuration LiDAR QA/QC: DPRG Approach

Ayman F. Habib, 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, Strip Number Flying Height Direction mN-S mS-N 3539 mE-W 4539 mW-E 5539 mE-W 6539 mE-W Experimental Results Data Captured by ALS50

Ayman F. Habib, Overlapping Strip Pairs Overlap %Direction Strips 1&280%Opposite directions Strips 3&425%Opposite directions Strips 4&575%Opposite directions Strips 5&650%Same direction Experimental Results

Ayman F. Habib, Experimental Results Estimated biases in the system parameters

Ayman F. Habib, Experimental Results Original Point Cloud Impact on Generated Profiles

Ayman F. Habib, Experimental Results Adjusted Point Cloud Impact on Generated Profiles

Ayman F. Habib, 2010 Before Calibration After Calibration Strips 1&2 X T (m)Y T (m)Z T (m) X T (m)Y T (m)Z T (m)  (deg) φ (deg) κ (deg)  (deg) φ (deg) κ (deg) Strips 3&4 X T (m)Y T (m)Z T (m)X T (m)Y T (m)Z T (m)  (deg) φ (deg) κ (deg)  (deg) φ (deg) κ (deg) Compatibility between overlapping strips before and after the calibration procedure 31 Experimental Results Impact on Existing Discrepancies

Ayman F. Habib, 2010 Photogrammetric Data Camera modelRollei P-65 Array dimension8984x6732 pixels Pixel size6μm6μm Nominal focal length60mm Camera classification Normal Angle Camera (AFOV = 58.6 º ) Camera Specifications Six flight lines: Four parallel flight 550m (50% side lap) Two opposite flight 1200m (100% side lap) Impact on Absolute Accuracy Experimental Results

Ayman F. Habib, Before CalibrationAfter Calibration Mean ΔX (m) Mean ΔY (m) Mean ΔZ (m) σ X (m) σ Y (m) σ Z (m) RMSE X (m) RMSE Y (m) RMSE Z (m) RMSE TOTAL (m) RMSE analysis of the photogrammetric check points using extracted control linear features from the LiDAR data before and after the calibration procedure Experimental Results Impact on Absolute Accuracy

Ayman F. Habib, 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, Comments and Questions?