Artificial Intelligence Center, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27 Prague 6, Czech Republic.
Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China.
Sensors (Basel). 2022 Nov 16;22(22):8855. doi: 10.3390/s22228855.
The performance of deep learning-based detection methods has made them an attractive option for robotic perception. However, their training typically requires large volumes of data containing all the various situations the robots may potentially encounter during their routine operation. Thus, the workforce required for data collection and annotation is a significant bottleneck when deploying robots in the real world. This applies especially to outdoor deployments, where robots have to face various adverse weather conditions. We present a method that allows an independent car tansporter to train its neural networks for vehicle detection without human supervision or annotation. We provide the robot with a hand-coded algorithm for detecting cars in LiDAR scans in favourable weather conditions and complement this algorithm with a tracking method and a weather simulator. As the robot traverses its environment, it can collect data samples, which can be subsequently processed into training samples for the neural networks. As the tracking method is applied offline, it can exploit the detections made both before the currently processed scan and any subsequent future detections of the current scene, meaning the quality of annotations is in excess of those of the raw detections. Along with the acquisition of the labels, the weather simulator is able to alter the raw sensory data, which are then fed into the neural network together with the labels. We show how this pipeline, being run in an offline fashion, can exploit off-the-shelf weather simulation for the auto-labelling training scheme in a simulator-in-the-loop manner. We show how such a framework produces an effective detector and how the weather simulator-in-the-loop is beneficial for the robustness of the detector. Thus, our automatic data annotation pipeline significantly reduces not only the data annotation but also the data collection effort. This allows the integration of deep learning algorithms into existing robotic systems without the need for tedious data annotation and collection in all possible situations. Moreover, the method provides annotated datasets that can be used to develop other methods. To promote the reproducibility of our research, we provide our datasets, codes and models online.
深度学习检测方法的性能使其成为机器人感知的一个有吸引力的选择。然而，它们的训练通常需要大量包含机器人在日常操作中可能遇到的所有各种情况的数据。因此，在现实世界中部署机器人时，数据收集和注释所需的劳动力是一个重大瓶颈。这尤其适用于户外部署，机器人必须在各种恶劣天气条件下工作。我们提出了一种方法，使独立的汽车运输车能够在没有人工监督或注释的情况下训练其用于车辆检测的神经网络。我们为机器人提供了一种用于在有利天气条件下检测激光雷达扫描中汽车的手工编码算法，并使用跟踪方法和天气模拟器来补充该算法。当机器人遍历其环境时，它可以收集数据样本，这些样本随后可以处理为神经网络的训练样本。由于跟踪方法是离线应用的，因此它可以利用在当前处理的扫描之前和当前场景的任何后续未来检测中做出的检测，这意味着注释的质量超过了原始检测的质量。随着标签的获取，天气模拟器能够改变原始感官数据，然后将其与标签一起输入神经网络。我们展示了这种在离线方式下运行的管道如何能够利用现成的天气模拟在闭环仿真器中为自动标记训练方案提供支持。我们展示了这种框架如何产生有效的检测器，以及天气模拟器在环路中如何有利于检测器的鲁棒性。因此，我们的自动数据注释管道不仅大大减少了数据注释，而且还减少了数据收集工作。这使得可以将深度学习算法集成到现有的机器人系统中，而无需在所有可能的情况下进行繁琐的数据注释和收集。此外，该方法提供了带注释的数据集，可用于开发其他方法。为了促进我们研究的可重复性，我们在线提供了我们的数据集、代码和模型。
