LiDAR Georeferencing and Boresight Calibration Software
OxTS Georeferencer is a simple and powerful LiDAR georeferencing software that combines OxTS INS data with LiDAR scanners to output highly accurate 3D point clouds.
OxTS Georeferencer is a software tool developed by OxTS to combine INS trajectory data with raw LiDAR data to create a georeferenced 3D Pointcloud.
OxTS Georeferencer takes a file collected with a LiDAR scanner (synchronised in real-time with an OxTS inertial navigation system), a processed navigation trajectory file from the OxTS INS, and some required configuration files, to create an LAS point cloud file that can be viewed in many 3rd party Lidar software packages.
Thanks to our experience in calibration techniques, we have been able to develop a solution that helps system integrators and end-users to start their work with confidence that their INS and LiDAR hardware has been installed precisely and accurately.
Boresight calibration is an essential part of INS & LiDAR integration.
If the coordinate systems of the two devices do not match perfectly then the georeferencing of LiDAR data will diverge over longer ranges and result in a less accurate point cloud.
OxTS Georeferencer includes the ability to boresight supported LiDAR devices in order to ensure the user can produce clean and reliable data every time.
The results of running a boresight calibration are sometimes very clear, and sometimes marginal, but the objective is the same – align all sensors on the vehicle to the same coordinate system to ensure a high level of accuracy, regardless of a predefined test scenario.
A boresighted system allows for complete flexibility over the way you perform a survey.
OxTS Georeferencer’s main function is to synchronise and fuse the OxTS INS and LiDAR data sets.
A LiDAR sensor can sense range to a target, but it has no internal knowledge of when or where it is, or where it has moved to since the last scan occurred. This is where the INS adds essential information:
Thanks to live accuracies being reported as well, the data can be intelligently filtered based on estimated position/orientation accuracy of the INS, allowing you to perform less clean-up of the data in post-processing.
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