Backpack and Pedestrian Lidar Processing

This workflow covers processing raw, pedestrian-acquired lidar data from a native-Phoenix system. Typically pedestrian lidar acquisition is performed using a backpack mount. This workflow covers the conventional lidar production workflow, where a processed trajectory is created via inertial processing and used to build a point cloud. SLAM processing can also be used to process backpack-acquired data sets, and may produce more accurate results when GNSS availability is limited - see the SLAM workflow here.

  1. If using a RECON system, open one of the DATA files. SpatialExplorer will detect the other associated DATA files, and then will proceed to extract lidar, imagery, and navigation data.

  2. Open the PLP file. After opening the PLP file, select a CRS for the project.

  3. If working with data from a Riegl lidar scanner, convert the RXP files to SDCX files.

  4. Import a processed trajectory (CTS, CLS, SBET, POF). If you do not yet have a processed trajectory, produce one using either InertialExplorer, NavLab embedded, or NavLab via LiDARMill.

    1. When processing data acquired via a pedestrian platform, it is recommended to use the Pedestrian dynamics profile in NavLab or InertialExplorer.

  5. Configure processing settings for the lidar. Consider a 360 degree field of view, and a minimum range of 0.5 meters.

  6. Create processing intervals (these intervals will also apply to imagery).

  7. Visually check lidar relative accuracy and determine what degree and type of optimization needs to be performed. Consider reviewing trajectory accuracy reports to determine what trajectory parameters (X,Y,Z, yaw, pitch, roll) require optimization. It's not uncommon to solve for all parameters with pedestrian data sets.

  8. Run LiDARSnap and optimize for necessary parameters. Typically, the LiDARSnap Mobile Trajectory Optimization preset works well with Pedestrian data sets. Consider whether ground control points should be enabled with LiDARSnap, to act as a vertical constraint.

  9. If ground control is available, compute residuals from point cloud to control points to determine what rigid adjustment, if any, is needed to match lidar elevations to ground control elevations.

  10. Perform any additional classification necessary. Users can make use of the Classify on Selection window, as well as some automated classification routines.

  11. Generate accuracy reports.

  12. Generate deliverables such as rasters (RGB raster, DTM, DEM) and vector deliverables (contours and meshes).

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