Characterizing the influence of surface roughness and inclination on  3D vision sensor performance

John R. Hodgson; Peter Kinnell; Laura Justham; Michael R. Jackson

doi:10.1117/12.2228826

8 December 2015 Characterizing the influence of surface roughness and inclination on 3D vision sensor performance

John R. Hodgson, Peter Kinnell, Laura Justham, Michael R. Jackson

Author Affiliations +

Proceedings Volume 9875, Eighth International Conference on Machine Vision (ICMV 2015); 98751L (2015) https://doi.org/10.1117/12.2228826
Event: Eighth International Conference on Machine Vision, 2015, Barcelona, Spain

Abstract

This paper reports a methodology to evaluate the performance of 3D scanners, focusing on the influence of surface roughness and inclination on the number of acquired data points and measurement noise. Point clouds were captured of samples mounted on a robotic pan-tilt stage using an Ensenso active stereo 3D scanner. The samples have isotropic texture and range in surface roughness (Ra) from 0.09 to 0.46 μm. By extracting the point cloud quality indicators, point density and standard deviation, at a multitude of inclinations, maps of scanner performance are created. These maps highlight the performance envelopes of the sensor, the aim being to predict and compare scanner performance on real-world surfaces, rather than idealistic artifacts. The results highlight the need to characterize 3D vision sensors by their measurement limits as well as best-case performance, determined either by theoretical calculation or measurements in ideal circumstances.

Citation Download Citation

John R. Hodgson, Peter Kinnell, Laura Justham, and Michael R. Jackson "Characterizing the influence of surface roughness and inclination on 3D vision sensor performance", Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 98751L (8 December 2015); https://doi.org/10.1117/12.2228826

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