Probabilistic Absolute Position Sensor Based on Objective Laser Speckles


This paper presents a vision-primarily based probabilistic absolute position sensor, that is in a position to operate on spacious technical surfaces while not the modification of the surface. Rather than using artificial surface markings, the surface structure itself is employed to get objective laser speckle patterns (OLSPs), that are observable on most technical surfaces. This permits the extraction of features on surfaces, that would possibly be too swish for image processing underneath white lightweight observation. Additionally, the individuality of the individually observed laser speckle patterns allows a determination of absolutely the position even once a sudden power loss of the sensor system. Experiments ensure the applicability of such OLSPs inside a feature-based probabilistic framework on a smooth stainless-steel rod and compare the results to photographs taken beneath white light illumination. The proposed sensor system successfully recovers from unknown initial states and measures the position with a peak-to-peak error of and an rms error of five.half dozen over a measurement vary of 100 mm.

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