Article
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High Precision Positioning and Rotation Angle Estimation of Flatbed Truck Based on Beidou and Vision
Version 1
: Received: 7 February 2024 / Approved: 8 February 2024 / Online: 8 February 2024 (05:11:45 CET)
A peer-reviewed article of this Preprint also exists.
Yu, X.; Ren, Y.; Yin, X.; Meng, D.; Zhang, H. High Precision Positioning and Rotation Angle Estimation of a Flatbed Truck Based on BDS and Vision. Sensors 2024, 24, 1826. Yu, X.; Ren, Y.; Yin, X.; Meng, D.; Zhang, H. High Precision Positioning and Rotation Angle Estimation of a Flatbed Truck Based on BDS and Vision. Sensors 2024, 24, 1826.
Abstract
Centimeter-level localization and rotation angle estimation of flatbed trucks are major problems in unmanned forklift automated loading scenes. To solve it, this study proposes a method for high-precision positioning and rotation angle estimation of flatbed trucks based on BeiDou and vision. First, an unmanned forklift equipped with a ToF camera and double antenna mobile receiver for BeiDou positioning collects depth images and localization data near the front and rear endpoints of the flatbed. Then DDRNet-23-slim was used to segment the flatbed from the depth image and extract the straight lines at the edges of the flatbed using the Hough transform and compute the set of intersection points of the lines. A neighborhood feature vector was designed to locate the endpoint of a flatbed from a set of intersection points by feature screening. Finally, the relative coordinates of the endpoints were converted to a customized forklift navigation coordinate system by BeiDou positioning, and rotation angle estimation was performed by the front and rear endpoints. Experiments showed that the endpoint positioning error was less than 3 cm and the rotation angle estimation error was less than 0.1°. The precision of the method meets the demand for automated flatbed truck loading by unmanned forklifts.
Keywords
unmanned forklift; automated loading; feature construction; Beidou and vision localization; rotation angle estimation
Subject
Engineering, Transportation Science and Technology
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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