论文标题
使用RGB-D数据进行机器人轮椅的自我监督可驾驶的区域和道路异常分割
Self-Supervised Drivable Area and Road Anomaly Segmentation using RGB-D Data for Robotic Wheelchairs
论文作者
论文摘要
可驱动区域和道路异常的分割是实现机器人轮椅自动导航的关键功能。使用深度学习技术的语义分割的最新进展提出了有效的结果。但是，用手工标记的地面真理收购大规模数据集是耗时且劳动密集型的，这使得基于深度学习的方法通常在实践中很难实施。我们通过提出一种自我监督的学习方法来为解决该问题的解决方案做出解决方案。我们开发一条可以自动生成可驱动区域和道路异常的分段标签的管道。然后，我们训练基于RGB-D数据的语义分割神经网络并获得预测标签。实验结果表明，与手动标记相比，我们提出的自动标签管道实现了令人印象深刻的加速。此外，与最先进的传统算法以及最先进的自我监督算法相比，我们提出的自我监督方法表现出更强和准确的结果。
The segmentation of drivable areas and road anomalies are critical capabilities to achieve autonomous navigation for robotic wheelchairs. The recent progress of semantic segmentation using deep learning techniques has presented effective results. However, the acquisition of large-scale datasets with hand-labeled ground truth is time-consuming and labor-intensive, making the deep learning-based methods often hard to implement in practice. We contribute to the solution of this problem for the task of drivable area and road anomaly segmentation by proposing a self-supervised learning approach. We develop a pipeline that can automatically generate segmentation labels for drivable areas and road anomalies. Then, we train RGB-D data-based semantic segmentation neural networks and get predicted labels. Experimental results show that our proposed automatic labeling pipeline achieves an impressive speed-up compared to manual labeling. In addition, our proposed self-supervised approach exhibits more robust and accurate results than the state-of-the-art traditional algorithms as well as the state-of-the-art self-supervised algorithms.
