Synthetic data augmentation for robotic mobility aids to support blind and low vision people

TL;DR

This paper explores the use of synthetic data generated with Unreal Engine 4 to improve vision models for robotic mobility aids assisting blind and low-vision individuals, addressing data scarcity issues.

Contribution

It demonstrates the effectiveness of synthetic data in training robust vision models and provides insights into optimizing synthetic data generation for assistive robotics.

Findings

Synthetic data improves model performance on multiple tasks

Synthetic data has limitations compared to real-world data

Public dataset release supports further research

Abstract

Robotic mobility aids for blind and low-vision (BLV) individuals rely heavily on deep learning-based vision models specialized for various navigational tasks. However, the performance of these models is often constrained by the availability and diversity of real-world datasets, which are challenging to collect in sufficient quantities for different tasks. In this study, we investigate the effectiveness of synthetic data, generated using Unreal Engine 4, for training robust vision models for this safety-critical application. Our findings demonstrate that synthetic data can enhance model performance across multiple tasks, showcasing both its potential and its limitations when compared to real-world data. We offer valuable insights into optimizing synthetic data generation for developing robotic mobility aids. Additionally, we publicly release our generated synthetic dataset to support ongoing research in assistive technologies for BLV individuals, available at https://hchlhwang.github.io/SToP.