https://doi.org/10.1140/epjs/s11734-024-01452-4
Regular Article
Real-time distance estimation algorithm for objects in warehouse based on monocular camera data for an autonomous unmanned forklift
Moscow Institute of Physics and Technology, 9 Institutskiy per., 141701, Dolgoprudny, Moscow Region, Russia
Received:
30
October
2024
Accepted:
12
December
2024
Published online:
7
January
2025
Accurate object recognition and distance calculation to objects of interest are critical issues in the field of autonomous unmanned systems. This paper proposes an algorithm to estimate the distance to pallets and loading/unloading zones for an automated unmanned forklift. The algorithm is based on a preliminary calibration of the camera and computer vision methods to recognize objects of interest. A geometric model for the operation of a monocular camera was implemented, allowing the calculation of the theoretical error of the proposed algorithm, which ranges from 0.05 to 0.21 m depending on the distance of the object from the camera. The developed algorithm was tested on a model of an unmanned forklift, using a Jetson Orin NX as the computing device. The real-time video stream processing speed reached 20–25 FPS. The experiment results showed that the distance calculation error to objects using the developed algorithm falls within the acceptable error margin, which is 10% of the distance from the object to the camera. The research demonstrated that the developed algorithm could be applied for calculating the distance to pallets for an automated forklift.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
