Calculation of discrete correlation functionin facet systems of techical vision
Abstract
The paper proposes a facet vision system composed from identical facet elements. Each facet element contains an optical system, several photodetectors, a preamplifier, and a universal microcontroller. In such a system, all facet elements operate independently of each other. Each facet element performs fast measurements of the angular velocity of objects in its field of view by calculating the discrete correlation functions of the signals from the photodetectors. The paper considers the possibility of using economical microcontrollers in facet elements for fast calculation of the discrete correlation functions. The authors perform a comparative analysis of the techniques based on the direct calculation and the calculation with fast Fourier transform. The investigation of the corresponding program code for microcontrollers in assembly language is done with calculations of the number of machine instructions and their execution time. The study confirms that economical universal microcontrollers are able to perform fast measurements by finding the maximum values of discrete correlation functions. In the case of receiving signals from 4 photodetectors, the calculation time is less than 10 milliseconds for input data arrays of 384 elements and less than 1.2 milliseconds for input data arrays of 128 elements. These results make the proposed facet vision systems applicable in navigation, orientation, and collision avoidance with moving and stationary objects in automatic vehicles, including unmanned aerial vehicles.
References
Custers B. The Future of Drone Use. Hague: TMC Asser Press, 2016. https://doi.org/10.1007/978-94-6265-132-6
D'Andrea R. Can drones deliver? IEEE Trans. on Automation Science and Engineering, 2014, no. 11(3), pp. 647-648. https://doi.org/10.1109/TASE.2014.2326952
Gallego G., Delbruck T., Orchard G. at al. Event-based vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, vol. 44, iss. 1, pp. 154-180. https://doi.org/10.1109/TPAMI.2020.3008413
Barrios-Aviles J., Iakymchuk T., Samaniego J. et al. Movement detection with event-based cameras: Comparison with frame-based cameras in robot object tracking using powerlink communication. Electronics, 2018, no. 7(11), 304, pp. 1-19. https://doi.org/10.3390/electronics7110304
Doge J., Hoppe C., Reichel P. et al. A 1 megapixel HDR image sensor soc with highly parallel mixed-signal processing. Proc. of 2015 IISW, 2015, Vaals, Netherlands. https://bit.ly/3xLBdHl
Franceschini N. Small brains, smart machines: from fly vision to robot vision and back again. Proc. of IEEE, 2014, no. 102, pp. 1-31. https://doi.org/10.1109/JPROC.2014.2312916
Leitel R., Bruckner A., Buss W. et al. Curved artificial compound-eyes for autonomous navigation. Proc. of SPIE, 2014, vol. 9130, pp. 91300H. https://doi.org/10.1117/12.2052710
Colonnier F., Ramirez-Martinez S., Viollet S. et al. A bio-inspired sighted robot chases like a hoverfly. Bioinspiration&Biomimetics, 2019, vol. 14, no. 3, 036002. https://10.1088/1748-3190/aaffa4
Juston R., Viollet S. A miniature bio-inspired position sensing device for the control of micro-aerial robots. Proc. of IEEE/RSJ, 2012, Vilamoura, pp. 1118-1124. http://dx.doi.org/10.1109/IROS.2012.6385937
Colonnier F., Manecy A., Juston R. at al. A small-scale hyperacute compound eye featuring active eye tremor: Application to visual stabilization, target tracking, and short-range odometry. Bioinspiration&biomimetics, 2015, no. 10(2), 026002. https://doi.org/10.1088/1748-3190/10/2/026002
Zhao J., Hu C., Zhang C. et al. A bio-inspired collision detector for small quadcopter. Proc. of IJCNN 2018, 2018, pp. 1-7. https://doi.org/10.1109/IJCNN.2018.8489298
Ruffier F., Viollet S., Amic S. et al. Bio-inspired optical flow circuits for the visual guidance of micro air vehicles. Proc. of ISCAS 2003, 2003, pp. III-III. https://doi.org/10.1109/ISCAS.2003.1205152
Aubepart F., Serres J., Dilly A. et al. Field programmable gate array (FPGA) for bio-inspired visio-motor control systems applied to micro-air vehicles. Aerial Vehicles. Delph, InTech, 2009, https://doi.org/10.5772/6466
Mockel R. Bio-Inspired Optical Flow Vision Sensors for Visual Guidance of Autonomous Robots. Dissertation ... Doctor of Sciences – DISS. ETH no. 20736, 1981, 270 p. https://www.research-collection.ethz.ch/handle/20.500.11850/153958
Baklitskiy V.K. Korrelyatsionno-ekstremal'nyye metody navigatsii i navedeniya [Correlation-extreme methods of navigation and guidance]. Russia, Tver, Publisher book club, 2009, 360 p. (Rus)
Borovytsky V., Antonenko V. [Speed sensor for unmanned aerial vehicle] Pat.UA no. 142 242, 2020, bul. 14 (Ukr)
Borovytsky V., Antonenko V. Biologically inspired compound eye. Proc. SPIE, 2020, vol. 11369, СЂp. 113691T. https://doi.org/10.1117/12.2553678
Valavanis K.P., Vachtsevanos G. J. Handbook of Unmanned Aerial Vehicles. Heidelberg, Springer Publishing Company Inc., 2014, 3022 p. https://doi.org/10.5555/2692452
Nussbaumer G. Bystroye preobrazovaniye Fur'ye i algoritmy vychisleniya sv'ortok [Fast Fourier Transform and Convolution Algorithms]. Russia, Moscow, Radio and communications, 1985, 248 p. (Rus)
Microchip Technology Inc. 8-bits MCUs. AVRВ® MCUs, https://www.microchip.com/en-us/products/microcontrollers-and-microprocessors/8-bit-mcus/avr-mcus
STMicroelectronics. STM32F103, https://www.st.com/en/microcontrollers-microprocessors/stm32f103.html
Revich Yu. V. Prakticheskoye programmirovaniye mikrokontrollerov Atmel AVR na yazyke assemblera [Practical programming of Atmel AVR microcontrollers in assembly language]. Russia, Saint-Petersburg, BHV-Petersburg, 2011, 352 p. (Rus)
Kozachenko V. F. Prakticheskiy kurs mikroprotsessornoy tekhniki na baze protsessornykh yader ARM-Cortex-M3/M4/M4F [Practical course of microprocessor technology based on ARM-Cortex-M3/M4/M4F processor cores]. Russia, Moscow: MEI, 2019, 543 p. (Rus)
Roubieu F. L., Expert F., Boyron M. et al. A novel 1-gram insect based device measuring visual motion along 5 optical directions. Sensors, 2011 IEEE Proc., 2011, Limerick, Ireland. pp. 687-692, https://doi.org/10.1109/ICSENS.2011.6127157
Viel С., Viollet S. Fast normalized cross-correlation for measuring distance to objects using optic flow, applied for helicopter obstacle detection. Measurement, 2020, vol. 172, no. 5, pp. 108911-108921. https://doi.org/10.1016/j.measurement.2020.108911
Copyright (c) 2025 Borovytsky V. N., Antonenko V. A.
This work is licensed under a Creative Commons Attribution 4.0 International License.
