Obstacle Detection for Automated Guided Vehicles

Nowadays, produced cars are equipped with mechatronical actuators as well as with a wide range of sensors in order to realize driver assistance functions.These components could enable cars’ automation at low speeds on company premises, although autonomous driving in public traffic is still facing technical and legal challenges.For automating vehicles in an industrial environment a reliable obstacle detection system is required. State-of-the-art solution for protective devices in Automated Guided Vehicles is the distance measuring laser scanner. Since laser scanners are not basic equipment of today’s cars in contrast to monocameras mounted behind the windscreen, we develop a computer vision algorithm that is able to detect obstacles in camera images reliably. Therefore, we make use of our well-known operational design domain by teaching an anomaly detection how the vehicle path should look like.The result is an anomaly detection algorithm that consists of a pre-trained feature extractor and a shallow classifier, modelling the probability of occurrence. We record a data set of a real industrial environment and show a robust classifier after training the algorithm with images of only one run.

当前量产乘用车已搭载机电执行器与多类传感器，以实现驾驶员辅助功能。尽管公共道路场景下的自动驾驶仍面临技术与法律层面的双重挑战，但此类组件可支持车辆在企业园区内低速场景下实现自动化行驶。针对工业场景下的车辆自动化作业，需搭建可靠的障碍物检测系统。目前自动导引车（Automated Guided Vehicles，AGV）防护装置的主流方案为测距激光扫描仪。但与安装于挡风玻璃后方的单目摄像头不同，激光扫描仪并非当前量产车的标配设备，因此我们研发了一套可在摄像头图像中可靠检测障碍物的计算机视觉算法。为此，我们依托成熟的运行设计域（operational design domain），通过异常检测模型学习车辆通行路径的标准形态。最终得到的异常检测算法由预训练特征提取器与浅层分类器构成，用于对目标出现的概率进行建模。我们采集了真实工业场景下的数据集，并通过仅单次行驶采集的图像完成算法训练后，验证得到了性能鲁棒的分类器。

搜集汇总

背景与挑战

背景概述

该数据集专注于自动导引车在工业环境中的障碍物检测问题，基于真实工业场景记录，用于支持计算机视觉算法的开发和验证。其核心特点是采用异常检测方法，通过预训练特征提取器和浅层分类器建模路径正常状态，仅需单次运行图像训练即可实现鲁棒的障碍物分类，适用于低速自动化车辆应用。