1. Prepare Your Computer
For L1 data processing, the computer must be equipped with an NVIDIA graphics card and at least 4 GB of VRAM. The CPU must be i5 or above, and 4 GB of memory is required for storing every 1 GB of raw point cloud files. You can refer to the table below to determine the size of raw point cloud data to be processed by Terra.
All L1 LiDAR data processing functions are free of charge except point cloud accuracy optimization. You can download and install the software from the DJI website:
Optimize Point Cloud Accuracy: When enabled, DJI Terra will optimize point cloud data collected at different times during processing for higher overall consistency. This is a premium feature included in DJI Terra Pro and more advanced versions. Each L1 comes with a 6-month license for the Electricity version. You can find the license information in the package. Note: With the complimentary DJI Terra license, you can only bind 1 device and cannot unbind it.
2. Reconstruction Steps
(1) Create a mission and select "LiDAR Point Cloud Processing" as the mission type.
(2) Click to add LiDAR point cloud data as a folder.
The selected folder should include files with the suffixes CLC, CLI, CMI, IMU, LDR, RTB, RTK, RTL, and RTS. JPG photos are not required.
How to merge the data from multiple flights into one LAS file?
You can put the data folders of multiple flights into one directory, and select this parent folder that contains the data files. In this way you will get only one LAS file.
Alternatively, you can select multiple L1 data file folders for processing. Then you will get a LAS file for each folder.
(3) Select Point Cloud Density
Three density levels are available: high, medium, and low, corresponding to 100%, 25%, and 6.25% of the point cloud data for processing. Point cloud density only affects the number of result points. It does not have a significant impact on the accuracy of the result.
(4) Output Coordinate System Settings
Set the coordinate system according to the project requirement.
Most point cloud analysis software does not allow users to view LAS point clouds in a geodetic coordinate system. If you've selected the geodetic coordinate system as the output coordinate system (such as WGS84: EPSG 4326), the result may not be shown properly when you open it with third-party software (such as CloudCompare or LiDAR360). To avoid this issue, you need to select a projection system as the output coordinate system.
Altitude settings: The Default setting is ellipsoidal height. You may change it to EGM96 height or other options.
(5) Parameter Settings
Point cloud effective distance: The cloud points with a distance greater than the set value from the LiDAR emission center will be filtered out during post-processing. The default value of this parameter is 250 meters. Do not set it too low or most of the points will be filtered out.
Optimize Point Cloud Accuracy: This function optimizes the adjustment of the point cloud data scanned at different times to improve the overall accuracy. It is recommended to keep this function enable for surveying and mapping purposes. However, for power line reconstruction, this option is recommended to be turned off.
Also if efficiency matters more than accuracy to you, this feature can be disabled, as the processing time can be significantly longer when it's enabled.
(6) Select the Reconstruction Output Format
3D point clouds in ONTS (the format used for display in DJI Terra) and LAS format (the standard format of airborne LiDAR output) will be selected by default. You can also choose to output the point cloud in PLY (for MeshLab), PCD (for CloudCompare), or S3MB (for SuperMap) format.
(7) Start Processing
Click "Start Processing" to start reconstruction. During processing, you can click "Stop" to pause the process, and the software will save the current progress. If you resume the process, the software will continue data processing from the breakpoint.
You can start multiple point cloud processing tasks at the same time. Before the first task is completed, the other tasks will be pending in the queue. Tasks are processed in the order they were started and will be started one by one after all prior tasks are completed.
(8) View Results
After the reconstruction is completed, you can move, zoom, rotate, or perform other actions on the result. You can also switch between different views:
RGB: Display the result in their true colors.
Intensity: Display the result based on the reflectivity received by the LiDAR, The reflectivity is graded on a scale of 0-255, where 0-150 corresponds to diffuse reflection objects with a reflectivity of 0-100%, and 151-255 corresponds to total reflectivity objects. Since light reflected by the same object may be received by the LiDAR at different angles, the reflectivity values of the same ground object may vary as shown on the reflectivity map. This is a normal phenomenon.
Height: Display different colors for different altitudes of the point cloud.
Return: Display different colors for result data of different echoes.
(9) Quality Report
The quality report for LiDAR point cloud processing is interpreted as follows:
3. Result Files
The result files output by DJI Terra include a LAS point cloud result and an OUT trajectory file.
*.las: The LAS point cloud output by DJI Terra is the standard result of airborne LiDAR. The LAS version is V1.2 and the file can be directly imported to most analysis software. The LAS result records information such as the 3D point coordinates, RGB color information, reflectivity, time, number of echoes, the echo that each 3D point belongs to, the total number of points in each echo, and the scan angle.
*_sbet.out: The post-processing trajectory file of the mission, which records the trajectory information after adjustment and solution. The file can be imported into third-party software for trajectory display. During DJI Terra's data processing, the point cloud accuracy optimization function is an adjustment process. For this reason, you don't need to perform a second adjustment using third-party software. The file is stored in binary format, and its data items, units, and types are as follows:
*_smrmsg.out: Post-processing precision file. It contains the root-mean-square error of the smoothed position, direction, and speed. Its data items, units, and types are as follows.
4.1 Error Message: The LiDAR point cloud POS data is abnormal.
Possible cause: The RTK was disconnected during the flight, or there was no RTK base station data available; or L1 was static/hovering in the air during data collection. Currently, DJI Terra doesn't support post-processing of data collection. Currently, DJI Terra doesn't support post-processing of data collected by a drone sitting still on the ground.
4.2 Error Message: The raw data is missing or the file path is wrong.
Possible cause: Some required raw files are missing. For example, the RTK base station data is missing as the RTK was turned off during the flight; or the file suffix is incorrect. Please refer to the first chapter in DJI L1 Field Operation Guide
to understand the detailed requirements for the RTK files.
4.3 Error Message: The raw data of the LiDAR point cloud is abnormal.
Possible cause: The collection time of the laser point cloud file (LDR) in the input path does not correspond to or does not overlap with the collection time of other files, which may be caused by mistakes in file copying.
4.4 Error Message: LiDAR point cloud accuracy optimization failed.
Possible cause: The flight altitude was too high, the speed was too fast, or the overlap was set too low, resulting in insufficient overlapping points for point cloud accuracy optimization. In this case, we recommend you increase the overlap and recollect the data, or disable "Point cloud accuracy optimization" when you don't require high accuracy.
4.5 Error Message: LiDAR point cloud POS calculation failed.
Possible cause: The content of some files in the input path is incorrect or data is missing; the time values in the IMU and RTK files (RTB, RTK, or RTL) do not overlap or match. In this case, you should check the data. You will probably need to recollect the data.
4.6 Issue: Quality of point cloud model is poor, or the result has severe data loss.
(1) Poor inertial navigation accuracy: Data collection started before the inertial navigation system completed warmed-up, or IMU calibration not performed correctly.
(2) Poor POS accuracy: The RTK was not fixed, the RTK base station was moved, or the coordinate systems of the self-built base station and multiple RTK base stations were inconsistent (for example, no known coordinates were configured for D-RTK 2).
(3) Low overlap: The collection time of the LiDAR point cloud file only partially overlaps with that of other input files, or the side overlap was set too low, In particular, in areas with fluctuating terrain, setting an excessively low lateral overlap can result in hallowing in the result.
(4) No objects scanned: For example, in a flight with gimbal pitch -90 degrees, the facades of a building may be hallowed die to the lack of scanned objects. In a power line scan, if the drone flies over the object only once, some power lines may be blocked by those above them at certain angles. In an area with significant elevation differences, some objects may fall out of the measurable range of L1.
(5) Low cutting distance of point cloud: The point cloud cutting distance set is less than the actual effective point cloud distance.
4.7 How to achieve a good vertical accuracy?
The following are important to achieve a good vertical accuracy:
- Make sure the RTK is always fixed;
- Set the base station on a known point if you are using D-RTK 2, make sure that you have considered the length of D-RTK 2 from bottom to top of antenna (it's 1.8m if the rod is fully extended to the ground); When setting the known coordinate and elevation in Pilot app, make sure to use WGS84 and ellipsoid height in unit m. Restart the M300 and D-RTK2 when there is a base station position change error in Pilot app.
- You may need to check the geoid height in the survey area.