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.

当前量产汽车均搭载机电执行器与多种传感器，以实现驾驶辅助功能。尽管公共道路上的自动驾驶仍面临技术与法律层面的挑战，但这些组件可支持汽车在公司厂区内低速运行时实现自动化。若要在工业环境中实现车辆自动化，则需搭建可靠的障碍物检测系统。目前自动导引车（Automated Guided Vehicles）防护装置的主流解决方案为测距激光扫描仪，但与安装在挡风玻璃后方的单目摄像头（monocamera）不同，测距激光扫描仪并非当前量产汽车的标配设备，因此我们开发了一款可在摄像头图像中可靠检测障碍物的计算机视觉（computer vision）算法。为此，我们依托成熟的操作设计域（operational design domain），通过异常检测（anomaly detection）模型学习车辆行驶路径的标准形态。最终得到的异常检测算法由预训练特征提取器（pre-trained feature extractor）与浅层分类器（shallow classifier）构成，可对障碍物的出现概率进行建模。我们采集了真实工业环境的数据集，并证明仅需使用单次采集的图像对算法进行训练，即可得到性能稳健的分类器。