基于目标和关键点检测的单目托盘定位

托盘的识别与定位是无人叉车中关键的问题之一.当前托盘定位多采用目标检测的方法,然而目标检测只能识别托盘在图像中的位置,无法得到托盘的空间信息.针对此问题,本文提出了一种基于目标和关键点检测的单目托盘定位方法,用于检测托盘并计算托盘当前的倾角和距离.首先对托盘进行目标检测,然后将检测的结果进行裁剪后输入到关键点检测网络中.通过对托盘关键点的检测和托盘固有的几何外形特征,设计边缘自适应调整,得到高精度的托盘轮廓信息.根据几何约束提出了基于轮廓点的托盘倾角与距离计算方法,并采用RANSAC算法提升了计算结果的精度和稳定性,解决了托盘的定位问题.实验表明,本文提出的算法在倾角计算上平均误差在5°以内,水平距离计算上平均误差在110 mm以内,能较好地定位托盘,具有较高的实用价值.

Pallet Positioning Based on Target and Key Points Detection with Monocular Vision

Pallet recognition and positioning is one of the critical problems in unmanned forklift trucks. At present, target detection is mostly used for pallet positioning. However, target detection can only recognize the position of the pallet in the image and cannot obtain the spatial information of the pallet. To solve this problem, this study proposes a pallet positioning method based on target and key point detection with monocular vision, which is applied to detect the pallet and calculate the current dip angle and distance of the pallet. Firstly, target detection is carried out on the pallet. Then, the image will be cropped according to the detection result and input into the key points detection network. Through the detection of the key points and the inherent geometric features of the pallet, the edge adaptive adjustment is designed to obtain the high-precision profile information of the pallet. According to the geometric constraints, a method for calculating the dip angle and distance of the pallet based on contour points is proposed, and the RANSAC algorithm is adopted to improve the precision and stability of the calculation results, thus addressing the problem of pallet positioning. Experiments indicate that the average error of the proposed algorithm is less than 5° in the calculation of dip angle and less than 110 mm in the calculation of horizontal distance. It works well for pallet positioning and is of high practical value.