Face it: sometimes your accuracy just hits the iceberg. We have seen error reports within 0.011 of a foot and we’ve seen them crest near 0.080 of a foot. The key to obtaining LiDAR accuracy is understanding the components.

When I speak to surveyors about the acronym RMS (root mean square) error reports, I often feel like a captain on a sinking ship. Esri’s definition is cumbersome: “A measure of the difference between locations that are known and locations that have been interpolated or digitized. RMS error is derived by squaring the differences between known and unknown points, adding those together, dividing that by the number of test points, and then taking the square root of that result.” The question is: Known and unknown to whom?

Think of it this way: RMS error is the difference between collected LiDAR measurements and traditional collected control points. Where your accuracy can tank is when you have an outlier in the LiDAR data set, not unlike a transposed coordinate number in the field book. But many other variables can affect the accuracy of the RMS error report.

In February of 2009, we scanned a highway in Minnesota using the DOT’s control points. The RMS error was off by inches; unacceptable even by traditional standards. Sifting painstakingly through the data by both the DOT and my Terrametrix technicians, it was determined that we were seeing frost heave. The control was set in warmer fall conditions and the scan was performed in the winter. Learning curve, and when we proved that point we were all amazed at being able to see this movement.

In the winter of 2016, Terrametrix mobile LiDAR collection on a mile-long interstate viaduct in the northern Mid-Atlantic region resulted in an RMS error of 0.060 compared to 64 control points ranging in a 0.090 high and 0.11 low on the bridge. Control set on hard ground at the approaches had a 0.013 RMS error against 34 control points ranging 0.037 high 0.034 low. The RMS error report comparisons were showing bridge load. Control was shot during rush hour; our scan was performed at the peak of heavy traffic load on the bridge. Control was set on the bridge mid-span and not as requested over the piers. You could tell because the error at each point was directly related to how far the control was from a pier with the greatest errors being near mid span.

A 2.9-mile road course at Watkins Glen in the fall of 2014 resulted in an RMS 0.012 range 0.023 high to 0.021 low against 15 control points every quarter mile. Not bad for an end deliverable used for re-pavement on a historical race track. Another DOT project in the fall of 2015 involved 10.6 miles of interstate. With 101 control points, the RMS error was 0.014; ranging 0.039 high to 0.030 low. These are typical RMS highway reports using mobile LiDAR.

I must warn anyone that boasts you don’t need survey control with LiDAR that it is like telling you the ship is unsinkable. What is your quality check? Control is, will be and always has been minimum standards and prudent operating procedure for land surveyors. Occupy the point. Also, be warned if the RMS error is too good. Anyone who comes up with a zero RMS error has adjusted the LiDAR data to the control point, plain and simple. Every component of measuring has error; the GPS has error, the scanners have error, the rodman may have had a bad night. (Come on, we’ve all been there). If your RMS error is zero, head for the life boats.