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

arXiv: https://arxiv.org/abs/2409.11164 · HTML: https://mlsanity.com/paper/2409.11164/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [YOLO-World: Real-Time Open-Vocabulary Object Detection](https://mlsanity.com/paper/2401.17270/) (2024)
- [Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks](https://mlsanity.com/paper/2311.06242/) (2023)
- [Towards Robust Robot 3D Perception in Urban Environments: The UT Campus Object Dataset](https://mlsanity.com/paper/2309.13549/) (2023)
- [Robotic Guide Dog: Leading a Human with Leash-Guided Hybrid Physical Interaction](https://mlsanity.com/paper/2103.14300/) (2021)
- [Is it safe to cross? Interpretable Risk Assessment with GPT-4V for Safety-Aware Street Crossing](https://mlsanity.com/paper/2402.06794/) (2024)
- [SANPO: A Scene Understanding, Accessibility and Human Navigation Dataset](https://mlsanity.com/paper/2309.12172/) (2023)
- [System Configuration and Navigation of a Guide Dog Robot: Toward Animal Guide Dog-Level Guiding Work](https://mlsanity.com/paper/2210.13368/) (2022)
- [Flying Guide Dog: Walkable Path Discovery for the Visually Impaired Utilizing Drones and Transformer-based Semantic Segmentation](https://mlsanity.com/paper/2108.07007/) (2021)
- [Street Crossing Aid Using Light-weight CNNs for the Visually Impaired](https://mlsanity.com/paper/1909.09598/) (2019)

## Abstract (arXiv)

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.