Self-Supervised Drivable Area and Road Anomaly Segmentation using RGB-D Data for Robotic Wheelchairs

TL;DR

This paper introduces a self-supervised learning pipeline for segmenting drivable areas and road anomalies in RGB-D data, significantly reducing labeling effort and improving robustness and accuracy for robotic wheelchair navigation.

Contribution

It presents an automatic labeling pipeline for semantic segmentation, enabling effective self-supervised training without large manually labeled datasets.

Findings

Automatic labeling pipeline speeds up data annotation process.

Self-supervised approach outperforms traditional algorithms.

Robust and accurate segmentation results achieved.

Abstract

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.