Filtering laser scan data - Remove NaN

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Hi, vbs. As far as I know, nan values could mean max_range (clean view) or min_range (obstacle two close). So, I think you need something else for your solution.

Hi fandrade. It was a valid assumption for my problem. Due to the high density of the laser-scans compared to the size of the obstacles I was dealing with, if an object got closer to the robot than min_distance it would be picked up by neighbouring readings of the scan anyway, thus not affecting my path planning algorithm. So it was safe to keep it simple and map the invalid reading to max_distance. More robust solutions would be to use an interpolation filter, as I mentioned in my answer, or a "valid average", where you just replace the invalid readings with the average value of their neighbouring readings that are not-NaN (or should I say aN? :).

Hi vbs, I think you found a nice solution. I am still dealing with this problem because I am only using 5 laser beams as the input of a deep neural network, where I am doing reinforcement learning, so when beams come with NaN, I cant figure out a way to interpolate o set a min/max value.