Processing Report

The LiDARMill Spatial Fuser processing report is intended for data processors to glean insight into details pertaining to specific processing pipeline configurations. This report focuses primarily on sensor calibration and data optimization details contributing to the the quality and accuracy of deliverable data products.

Processing

This section outlines key processing parameters

Camera

This section displays information pertaining to the Camera sensor used in the project. The # represents the number of sensors in the mission. The count begins at 0 and increases depending on the number of Camera sensors (e.g. Camera 0, Camera1, etc).

Camera # (Manufacturer and Model of camera used in the project)
Camera Calibration	Specifies whether camera calibration was enabled (ON) or disabled (OFF)
Use photos from turns	Specifies if user selected to enable photos to be utilized within turns (ON) or only utilize photos along straight flightline intervals (OFF)
Focal Length Input	The input focal length of the camera (mm). When the Calibrate Camera switch is enabled, this is the value the user manually specified as the starting point for an enabled camera calibration. For best results, the input value should be close to the focal length value stated by the len's manufacturer. When the Calibrate Camera switch is disabled, this is the value stored on the rover as the camera's focal length.

Camera configuration parameters used for RGB encoding

Transforms	TX,TY,TZ	RX,RY,RZ
IMU -> Sensor	The translations (X,Y and Z) along the IMU axis between the center of navigation (IMU reference point) and the camera reference point	The rotations between the IMU frame and the camera sensor frame (Z,X,Y order)
Sensor -> Receptor #	The translations (X,Y and Z) between the camera sensor to the camera receptor	Camera boresight misalignment corrections - IMU to receptor (roll, pitch, yaw)

Intrinsics	Width	Height	Pixels W	Pixels H	Principal Point	Focal Length (mm)
Receptor #	Image width in mm	Image height in mm	Image width in pixels	Image height in pixels	The x and y image coordinate of the principal point in pixels, measured from the origin	Focal length of camera (The resulting calibrated focal length when camera calibration switch is enabled with Spatial Fuser pipeline)

Lens Distortion	k1	k2	k3	p1	p2
Receptor #	Radial distortion of the lens k1	Radial distortion of the lens k2	Radial distortion of the lens k3	Tangential distortion of the lens p1	Tangential distortion of the lens p2

LiDAR

This section displays information pertaining to the LiDAR sensor used in the project. The # represents the number of sensors in the mission. The count begins at 0 and increases depending on the number of LiDAR sensors (e.g. LiDAR 0, LiDAR 1, etc).

Optimization/Calibration Options:

Trajectory optimization: This tool performs feature matching within overlapping swaths of LiDAR data to determine correction offsets that are applied to the mission's trajectory to improve point cloud relative accuracy. Enabled/disabled within Fuser pipeline configuration.

Sensor Calibration: This tool applies an angular correction to LiDAR sensor (pitch, yaw, roll correction) to resolve misalignments from IMU to sensor. Depending on the LiDAR model, an additional ranging scale correction, tilt angle offset, or encoder calibration correction may be calculated and applied. Enabled/disabled within Fuser pipeline configuration.

LiDAR # (LiDAR Make and Model)
Downward FOV	Specified downward Field of View of the LiDAR swath.
LiDAR Range	The Minimum Range, Maximum Range (in meters) from the LiDAR sensor (set for pointcloud production - often used to block close range noise)
Calibrate Laser Orientation	Specifies whether LiDAR sensor calibration was enabled (ON) or disabled (OFF)
Trajectory Optimization	Specifies whether Trajectory Optimization was enabled (ON) or disabled (OFF)

LiDAR Parameters Used for Pointcloud Creation

Transform	TX, TY, TZ	RX, RY, RZ
IMU -> Sensor (Measured)	The translations (X,Y and Z) along the IMU axis between the center of navigation (IMU reference point) and the LiDAR reference point (Pipeline input values)	The rotations between the IMU frame and the LiDAR sensor frame (Z,X,Y order)
IMU -> Sensor (Calibrated)	Translation corrections along the IMU axis between the center of navigation (IMU reference point) and the LiDAR reference point (LiDARMill derived calibration values)	LiDAR boresight misalignment corrections - IMU to sensor (roll, pitch, yaw)
Laser #	Per laser translation from center of LiDAR sensor to center of individual laser receptor	Per laser rotation about the IMU axis (roll, pitch, yaw)

Intrinsics	RangeScale	RangeOffset	ScanAngleScale	ScanAngleOffset	TiltAngleScale	TiltAngleOffset
Laser #	The applied ranging scale correction per laser.	The applied ranging offset correction per laser.	The applied scan angle scale correction per laser.	The applied scan angle offset correction per laser.	The applied tilt angle scale correction per laser.	The applied tilt angle offset correction per laser.

Sessions

Filename	File Size	FOV	Program	Rotational Velocity
Raw LiDAR file name used in point cloud creation	Raw LiDAR file size used in point cloud creation	LiDAR sensor FOV during acquisition	LiDAR sensor Pulse Repetition Rate during acquisition	LiDAR sensor mirror or rotational Rate during acquisition

LiDAR Calibration/Optimization

Relative Accuracy

Relative accuracy, the measure of how well overlapping flightlines match each other, is determined for the mission(s). Surface models are developed for each flightline. Relative accuracy is calculated from these surfaces using two metrics, magnitude and dZ. Magnitude is the average of the absolute values of the vertical offsets between a single flightline surface and points from overlapping flightlines. dZ is the average value of the vertical offsets between a single flightline surface and the points from overlapping flightlines. An average magnitude for all flightlines represents the project's overall relative accuracy.

Before Optimization

Average Magnitude	LiDAR project's overall relative accuracy before optimization

Flightline	Magnitude	Dz
Flightline interval #	Average of the absolute values of the vertical offsets between a single flightline surface and points from overlapping flightlines	Average value of the vertical offsets between a single flightline surface and the points from overlapping flightlines

After Optimization

Average Magnitude	LiDAR project's overall relative accuracy after optimization

Flightline	Magnitude	Dz
Flightline interval #	Average of the absolute values of the vertical offsets between a single flightline surface and points from overlapping flightlines	Average value of the vertical offsets between a single flightline surface and the points from overlapping flightlines

Cloud Calibration Report

Strip to Strip Calibration

A	B	count	mean	stddev	rmse
Index of first matched strip	Index of second matched strip count	Number of correspondences used for matching	The mean of per-strip-pair correspondence distances, using point to plane model	The standard deviation (one sigma) of per-strip-pair correspondence distances	The root mean square error of per-strip-pair correspondence distances
All = All strips combined	All = All strips combined	Accumulated count of all existing correspondences.	The mean of all existing correspondences.	The standard deviation of all existing correspondences.	The root mean square error of all existing correspondences.

Correspondence Histogram / Normal Distribution Graph

Histogram of Correspondence Distance Errors

A histogram showing the distribution of vertical distance error residuals between correspondences in overlapping strips of LiDAR. Correspondences are utilized for LiDAR calibration and/or optimization. The combination of a high correspondence count, low correspondence distance errors, and well distributed surface normal orientations is a good indication of a high quality calibration and/ or optimization.

Histogram of Correspondence Distance Errors
Count	Combined total of all correspondences
Mean	The mean of all existing correspondences.
StdDev	The standard deviation of all existing correspondences.
StdDevMAD	The Mean Absolute Deviation of all existing correspondences.

Normal Orientations Plot

A plot showing the surface normal orientations that were sampled during the Lidar calibration and/or optimization procedure. This is a quality control plot that represents the robustness of observations within the dataset used to perform LiDAR calibration and/or optimization.

Point Cloud Statistics

Points Per Second

Plot of the laser return count per laser as a function of time.

Reflectance Histogram

Plot of the distribution of reflectance values per laser.

Return Count Histogram

Plot of the distribution of laser return indexes (first, second, third, etc).

Trajectories

Filename of Post Processed Trajectory used for data product creation
Datum:	The datum used to post process the trajectory.
Epoch:	The epoch of the post processed trajectory as defined by the reference station input.
Time in Motion:	The approximate time spent by rover in motion throughout the mission trajectory file.
Detected Flightlines:	The number of straight flightlines within the mission processed.
Unoptimized Name:	The file name of the GNSS/Inertial post processed trajectory file.
Optimized Name:	The file name of the optimized post processed trajectory after trajectory optimization and GCP alignment have been applied.
Align to GCP - Pointcloud Transformation:	The delta East, North, and Up translation applied to the finalized point cloud. A translation is implemented automatically when a GCP file is uploaded, with one or more survey control points marked as "CONTROL" , and the file is enabled within the Spatial Fuser pipeline

Post Processed vs Optimized Differences

The first plot shows the difference between the Navlab post processed trajectory and the LiDARMill optimized trajectory in terms of north, east and up positional difference as a function of time. Optimized trajectories include vertical translations per flightline interval to improve relative accuracy. Optimized trajectories also account for survey CONTROL points, and a single vertical translation is applied to the post processed trajectory when CONTROL points are enabled, in order to best fit the LiDAR pointcloud to ground control.

The second plot shows the difference between the Navlab post processed trajectory and the LiDARMill optimized trajectory in terms of roll, pitch and heading as a function of time.

Maps

RGB

Pointcloud colored by corresponding RGB pixel values from mission imagery.

Intensity

Pointcloud colored by corresponding laser intensity.

Ellipsoidal Altitude

Pointcloud colored by corresponding ellipsoidal elevation values.

Height Above Ground

Pointcloud colored by distances above ground model derived from ground classified points

Strip Index

Pointcloud colored by strip number.

Strip Overlap

Pointcloud colored by number of overlapping strips.

Density

Pointcloud colored by point density

Classification

Pointcloud colored by Classification (top-down view).

Classification (Bottom-Up)

Pointcloud colored by classification (bottom-up view).

GCP Separation

Digital surface model with overlaid GCPs colored by dZ value.