Welcome

My thoughts with this actually went more in a process-after-extracting-cluster type algorithm for KNN in order to accurately interpolate what value to shoot at given a set of adjacent-in-n-space values. I think it would be much better than weighting scans by 1/distance or whatever other weighting scheme gets used, as the noise could be eliminated based on location instead of distance and only the trends would be chosen, much like how a histogram allows one to select the highest peak rather than just taking the mean of all the scans. I think it would require fairly large clusters (200 or so points at least), but it could net fairly large gains against the right data patterns.

That way of using is exactly what I was proposing years ago in the last major paragraph of oldwiki:Rednaxela/MultiplePlaneRegressionClustering, though I think you word it much more elegantly than I did :-)

("To use this data of the lines, you simply take every data point, shift it's GuessFactor by however much the formula for the plane it is clustered with says it should be shifted, and then use a kernel density algorithm on these shifted values....")