基于圆标记的单台通用相机机器学习定位算法

2018 IEEE International Conference of Safety Produce Informatization (IICSPI) Pub Date : 2018-12-01 DOI:10.1109/IICSPI.2018.8690487

Cheng Zerui, Huang Zizhao, Cai Zhigang, Sun Zihan, Wang Jiahui

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引用次数: 1

摘要

室内定位是仓储中的一个重要问题。目前，具有QR码定位系统的高效自动导引车(agv)在货物分拣中很受欢迎。它们的主要缺点是空间利用率低。为此，提出了基于圆标记的机器学习定位算法(CMLPA)——一种单目定位方法。与许多注重消除变形影响的系统相比，CMLPA利用梯度增强决策树算法基于圆标记来预测距离和位置。实验结果表明，在2m *2m的位置，CMLPA的平均绝对误差低至0.34 cm，最大误差为4 cm。因此，CMLPA在存储领域显示出巨大的潜力。

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Circle-Marker-Based Machine Learning Positioning Algorithm with Single General-Purpose Camera

Indoor positioning is an important issue in warehousing. Nowadays, efficient automated guided vehicles (AGVs) with QR codes positioning system are popular in cargo sorting. The main disadvantage of them is low space utilization. Therefore, Circle-Marker-Based Machine Learning Positioning Algorithm (CMLPA), a monocular positioning method, is presented. Contrast with many systems focusing on eliminating the influences of deformation, CMLPA utilizes it to predict the distance and the position based on circle-markers with gradient boosting decision trees algorithm. The experimental result demonstrates that the average absolute error of CMLPA is low to 0.34 cm in 2 m*2m site while the maximum error is 4 cm. Thus, CMLPA shows great potential in storing field.

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2018 IEEE International Conference of Safety Produce Informatization (IICSPI)

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