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.