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Large-volume NIR pattern projection sensor for continuous low-latency 3D measurements

: Munkelt, C.; Heinze, M.; Bräuer-Burchardt, C.; Kodgirwar, S.P.; Kühmstedt, P.; Notni, G.


Harding, K.G. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Dimensional Optical Metrology and Inspection for Practical Applications VIII : 16-17 April 2019, Baltimore, Maryland, United States
Bellingham, WA: SPIE, 2019 (Proceedings of SPIE 10991)
ISBN: 978-1-5106-2647-8
ISBN: 978-1-5106-2648-5
Paper 109910K, 7 S.
Conference "Dimensional Optical Metrology and Inspection for Practical Applications" <8, 2019, Baltimore/Md.>
Fraunhofer IOF ()

For continuous, low-latency, irritation-free 3D measurements in large-volumes, dot-pattern- or time-of-flight-based sensors have been traditionally used. However, measurement accuracy and temporal stability limits the application in demanding medical or industrial contexts. Practical solutions also need to remain cost-effective. To meet these requirements, we started from a simple GOBO-based, aperiodic sinusoidal pattern projection (using a near-infrared (NIR) LED) 3D sensor for medium-sized measurement volumes. By tuning the system for large-volume operation, we were able to obtain a reasonable combination of measurement accuracy and speed. The current realization covers a volume of up to 4.0 m x 2.2 m x 1.5 m (width x height x depth). The 3D data is acquired at < 20 fps at resolutions of < 1000 x 500 px and true end-to-end latencies of < 140 ms. We present the system architecture consisting of GigE Vision cameras, a high-power LED-driven projection unit using a GOBO wheel, and the compute backend for the online GPU-based, temporal pattern correlation 3D calculation and filtering. To compensate for the low pattern intensity due to the short exposure time, we operate the cameras in 2x2 binning. Furthermore, the optics are tuned for large apertures to maximize light throughput. We characterize the sensor system with respect to measurement quality by quantitative evaluations including probing error, sphere-spacing error, and flatness measurement error. By comparison with another 3D sensor as a baseline, we show the benefits of our approach. Finally, we present measurement examples from human-machine interface (HMI).