Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.
Department of Mechanical Engineering, National Taiwan University, Taipei 106319, Taiwan.
Sensors (Basel). 2022 Jun 23;22(13):4751. doi: 10.3390/s22134751.
Accurate segmentation of drivable areas and road obstacles is critical for autonomous mobile robots to navigate safely in indoor and outdoor environments. With the fast advancement of deep learning, mobile robots may now perform autonomous navigation based on what they learned in the learning phase. On the other hand, existing techniques often have low performance when confronted with complex situations since unfamiliar objects are not included in the training dataset. Additionally, the use of a large amount of labeled data is generally essential for training deep neural networks to achieve good performance, which is time-consuming and labor-intensive. Thus, this paper presents a solution to these issues by proposing a self-supervised learning method for the drivable areas and road anomaly segmentation. First, we propose the Automatic Generating Segmentation Label (AGSL) framework, which is an efficient system automatically generating segmentation labels for drivable areas and road anomalies by finding dissimilarities between the input and resynthesized image and localizing obstacles in the disparity map. Then, we train RGB-D datasets with a semantic segmentation network using self-generated ground truth labels derived from our method (AGSL labels) to get the pre-trained model. The results showed that our AGSL achieved high performance in labeling evaluation, and the pre-trained model also obtains certain confidence in real-time segmentation application on mobile robots.
准确地分割可行驶区域和道路障碍物对于自主移动机器人在室内和室外环境中安全导航至关重要。随着深度学习的快速发展，移动机器人现在可以基于它们在学习阶段学到的知识进行自主导航。另一方面，由于训练数据集中不包括不熟悉的物体，现有技术在面对复杂情况时往往性能较低。此外，通常需要使用大量标记数据来训练深度神经网络以实现良好的性能，这既耗时又费力。因此，本文通过提出一种用于可行驶区域和道路异常分割的自监督学习方法来解决这些问题。首先，我们提出了自动生成分割标签（AGSL）框架，这是一个高效的系统，通过在输入和重新合成图像之间找到差异，并在视差图中定位障碍物，自动为可行驶区域和道路异常生成分割标签。然后，我们使用从我们的方法（AGSL 标签）中获得的自我生成的地面真实标签训练 RGB-D 数据集的语义分割网络，以获得预训练模型。结果表明，我们的 AGSL 在标签评估中表现出了很高的性能，预训练模型在移动机器人上的实时分割应用中也获得了一定的置信度。
