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.