Разработка модуля компьютерного зрения для автономных транспортных средств
DOI:
https://doi.org/10.26577/JMMCS.2022.v116.i4.06Ключевые слова:
Компьютерное зрение, автономное транспортное средство, планирование траектории транспортного средства, планирование траектории в реальном времени, беспилотное решение.Аннотация
Выгодное геополитическое положение и огромный транзитный потенциал Республики Казахстан в сфере наземных грузоперевозок между Китаем и Европой делает отрасль
транспортной логистики одним из самых перспективных направлений развития экономики страны. В этом контексте, применения технологий беспилотного грузового транспорта для минимизации издержек от расходования топлива и использования человеческого труда в трудоёмких и рутинных операциях логистических процессов как внутри складских помещений, так и при грузоперевозках по дорогам общего пользования, видится как никогда естественным и эффективным.
В данной статье описаны результаты научно-исследовательской работы по разработке модуля компьютерного зрения для прототипа автономного грузового автомобиля. Выполненные этапы проекта включают в себя установку необходимого оборудования, обучение моделей компьютерного зрения и разработку сопоставления между камерами и датчиком LIDAR для целей классификации и локализации объектов.
Библиографические ссылки
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[2] Nazarbayev N., “State program «Strategy «Kazakhstan — 2050»: new political course of a developed country” , accessed: 07.07.2022, URL: https://online.zakon.kz.
[3] “What is the importance of logistics development for the economy of Kazakhstan” , accessed: 07.07.2022, URL: https://forbes.kz/finances/markets/birthday/.
[4] “Official website of the State program «Digital Kazakhstan»” , accessed: 07.07.2022, URL: https://digitalkz.kz.
[5] “Transport logistic centers of Kazakhstan, what was done?” , accessed: 07.07.2022, URL: http://atameken.kz/ru/articles/27077-transportkazakhstana.
[6] “Official website of Tesla” , accessed: 07.07.2022, URL: https://www.tesla.com/autopilot.
[7] “Nissan tests fully autonomous prototype technology on streets of Tokyo?” , accessed: 07.07.2022, URL: https://newsroom.nissan-global.com/releases/release-1fc537356ae3aaf048d0201b77013bf9.
[8] “Autonomous trucks in real operation” , accessed: 07.07.2022, URL: https://www.volvotrucks.com/en-en/news/volvotrucks-magazine/2019/feb/bronnoy.html.
[9] “Mining automation: The be all and end all?” , accessed: 07.07.2022, URL: https://www.australianmining.com.au/features/mining-automation-the-be-all-and-end-all/.
[10] “Robotized technique for mining and industrial companies” , accessed: 07.07.2022, URL: https://vistgroup.ru/solutions/robotizirovannaya-tekhnika/.
[11] “Self-Driving Trucks Are Now Delivering Refrigerators” , accessed: 07.07.2022, URL: https://www.wired.com/story/embark-self-driving-truck-deliveries/.
[12] “A new project for development of an autonomous vehicle based on KAMAZ NEO platform started at Nazarbayev University” , accessed: 07.07.2022, URL: https://nu.edu.kz/ru/news-ru/v-nazarbaev-universitete-startoval-proekt-posozdaniyu-robotizirovannogo-avtomobilya-kamaz-neo.
[13] Abilkassov Sh., Nurlybayev A., Soltan S., Kim A., Shpieva E., Yesmagambet N., Yessenbayev Zh., Shintemirov A., “Facilitating Autonomous Vehicle Research and Development Using Robot Simulators on the Example of a KAMAZ NEO Truck” , 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), (2020):1-8.
[14] Zhang Zh, “A Flexible New Technique for Camera Calibration” , IEEE Trans. Pattern Anal. Mach. Intell., 22(11), (2000):1330-1334.
[15] Abdel-Aziz Y.I., Karara H.M., “Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry” , In Proceedings of the Symposium on Close-Range Photogrammetry, 8 (1971):1-18.
[16] “cvlib - a simple, high level, easy to use, open source Computer Vision library for Python” , accessed: 07.07.2022, URL: https://www.cvlib.net/.
[17] “ArUco: a minimal library for Augmented Reality applications based on OpenCV” , accessed: 07.07.2022, URL: https://www.uco.es/investiga/grupos/ava/node/26.
[18] Lin T.Y., Maire M., Belongie S., Hays J., Perona P., Ramanan D., Doll?r P., Zitnick C.L., “Microsoft COCO: Common Objects in Context” , European conference on computer vision, (2014):740-755.
[19] “YoloMark - Windows and Linux GUI for marking bounded boxes of objects in images for training Yolo v3 and v2”, accessed: 07.07.2022, URL: https://github.com/AlexeyAB/Yolo_mark.
[20] “Russian Traffic Sign Dataset” , accessed: 07.07.2022, URL: http://graphics.cs.msu.ru/en/research/projects/rtsd.
[21] Redmon J., Divvala S., Girshick R., Farhadi A., “You only look once: Unified, real-time object detection” , In Proceedings of the IEEE conference on computer vision and pattern recognition, (2016):779-788.
[22] Duan K., Bai S., Xie L., Qi H., Huang Q.,Tian Q., “CenterNet: Keypoint Triplets for Object Detection” , In Proceedings of the IEEE/CVF international conference on computer vision, (2019):6569-6578.
[23] Geiger A., Lenz P., Stiller C., Urtasun R., “Vision meets robotics: The KITTI dataset” , International Journal of Robotics Results, 32(11)(2013):1231–1237.
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Как цитировать
Yessenbayev, Z., Kozhirbayev, Z., & Shintemirov, A. (2022). Разработка модуля компьютерного зрения для автономных транспортных средств. Вестник КазНУ. Серия математика, механика, информатика, 116(4). https://doi.org/10.26577/JMMCS.2022.v116.i4.06
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