NavLab Embedded

NavLab Embedded produces a refined trajectory using raw navigation data recorded by the lidar system. Typically, a GNSS reference station is also involved.

To process your trajectory using NavLab Embedded, you will need the following:

  1. GNSS reference station data loaded into project

  2. Raw NAV trajectory

  3. Internet Connection for some functionality

To process your trajectory, select the NavLab Embedded icon. Then, configure your Output Directory, Processing Datum, Output formats, Processing type, Profile, and Processing options:

Output directory: Output directory of files created by NavLab Embedded.

Processing datum: Typically, this should be the datum of your project's coordinate reference system. This is NOT necessarily your project coordinate system. NavLab embedded processes within a datum only (spherical coordinate system), so if you are using a projected coordinate system (UTM, State Plane, etc.), select only the datum associated with your coordinate system.

Output Formats: InertialExplorer is the default format. Users can optionally add SBET or SBTC output formats.

Processing Type: Processing type determines how GNSS data is processed (PPP or Differential) and how the INS solution is coupled (tightly coupled or loosely coupled). Typically, Differential GNSS and INS Tightly Coupled performs the best with aerial, mobile, and pedestrian data.

The Differential GNSS processing type generates only a processed GNSS file (.CG file), which has no attitude information and cannot be used to build a point cloud. Differential GNSS processing is typically only used for SLAM processing.

Any processing type that utilizes PPP GNSS processing does not require reference station data. PPP processing utilizes refined ephemeris products to re-compute the lidar's raw GNSS data. PPP processing results typically have a vertical RMS of about 5 - 10 cm, compared to a typical differential GNSS vertical RMS of 1 - 3 cm.

Profile: Select your IMU and profile (Airborne, mobile, UAV, etc.). With aerial data sets, typically the Airborne profile performs best, with the UAV profile being second best. Mobile data sets, collected using a ground vehicle, should use the Mobile profile.

Processing Options:

  • Multi-pass: processing is typically performed in the forward and reverse directions. Multi-pass will enable processing in the forward and reverse directions several times each and all results will be combined.

  • Enable Doppler: Vehicle velocity can be measured by observing the GNSS doppler effect. This should typically be enabled as it is important for determining the vehicle speed prior to alignment.

  • Precise files: Precise files are clock and ephemeris products produced by NovAtel. Typically, it's beneficial to download precise files.

  • Enable AR: This enables PPP-AR (Precise Point Positioning with Ambiguity Resolution). PPP-AR uses refined ephemeris and clock bias correction products to improve GNSS processing results when differential GNSS is not available (i.e. when a reference station is not used during processing). This feature requires an NRT license (see Tools->Licensing).

Start and End Times: These fields can be configured to clip the processing time range.

Body to IMU orientation and IMU to Antenna Lever Arm values are displayed near the bottom of the menu. If these parameters need to be modified, they should be configured in the project window GNSS and IMU entries.

If you are processing with differential GNSS, ensure your reference station position is displayed correctly.

Estimate Primary Lever Arms

NavLab computes X iterations (specified by user) and the resulting lever arm measurement convergence is plotted below, with the X axis showing iteration number, and the Y axis showing lever arm offsets colored by X,Y,Z lever arm calculations (Forward direction on the left, Reverse on the right). Note that Inertial Explorer's ability to estimate lever arm values is dependent on the quality of the initial lever arm measurement input, correct vehicle body rotation, the amount of data collected, and vehicle dynamics.

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