  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.
