Part I of this series described the lidar georeferencing process (for one, 2D, single return scanner). Part II showed how point uncertainties are computed and why they are useful.
What hasn’t been covered so far is what data you need to do this stuff! Here is your shopping list:
Scanner data
- Raw laser scanner ranges
- Scanner mirror angles
- Waveforms with times if you are going to decide range-to-returns for yourself
- Intensity
- anything else (RGB, extra bands, etc)
Essentially you need the full data package from the laser scanner including any corrections for mirror wobble or other sensor oddness – and a way to decode it. You also need engineering drawings which show the scanner’s reference point.
Aircraft trajectory data
- GPS/GNSS based aircraft trajectory (positions in space)
- Attitude data (relationship between the aircraft’s orientation and some reference frame) normally collected by a ‘strapdown navigator’ – which is an inertial motion sensor (often called IMU), and often integrated with GNSS.
Hopefully you don’t have to do the work combining these yourself, but you need a high-temporal-resolution data stream of position (XYZ) and attitude (heading pitch roll, or omega phi kappa, or a quaternion describing rotation of the airframe relative to the earth). This needs to be as accurate as possible (post-processed dual-frequency GPS with preferably tightly-coupled processing of IMU and GPS observations).
Navigation instrument and aircraft data
- Engineering drawings which show the navigation instrument’s reference point
- The lever arm between the navigation instrument reference point and the LIDAR reference point, in IMU-frame-coordinates
- The rotation matrix between the IMU and the LIDAR coordinate systems
- The rotation matrix describing the difference between the aircraft’s body frame and the IMU’s internal coordinate system (quite often, this gets simplified by assuming that the IMU frame and the aircraft frame are equivalent – but many operators still account for this – which is awesome!)
- Any boresight misalignment information you can get (tiny angles representing the difference between how we think instruments were mounted and how they actually were mounted)
Of course, you could also just push your contractors to deliver point-by-point uncertainty or some per-point quality factor as part of the end product…
Things we’ve missed, and final words
Instrument effects are not the end of the story. Another whole body of research exists on additional uncertainties when lidar points are collected on hard or soft surfaces, where light penetrates a substrate or not, where in a forest canopy a return comes from, and more…
My lidar work was all over sea ice using an infrared instrument – meaning that we had a pretty simplified set of concerns about uncertainty. I hope this series shows that there is a lot to consider when talking about laser scanners and measurement uncertainties.
In short, nobody can realistically claim the intersection of ‘centimetre level’ and ‘cheap’, not even at tens of metres from a target. Uncertainties exist along the whole chain of processing! Go forth and scan stuff – just be realistic about your claims around how accurate the resulting data are….
The sales pitch
Spatialised is a fully independent consulting business. Everything you see here is open for you to use and reuse, without ads. WordPress sets a few cookies for statistics aggregation, we use those to see how many visitors we got and how popular things are.
If you find the content here useful to your business or research or billion dollar startup idea, you can support production of ideas and open source geo-recipes via Paypal, hire me to do stuff; or hire me to talk about stuff.