From Gaming to Research: GTA V for Synthetic Data Generation for Robotics and Navigations

TL;DR

This paper introduces a synthetic dataset generated from GTA V for robotics and navigation tasks, demonstrating its effectiveness as a cost-efficient alternative to real-world data for SLAM and VPR applications.

Contribution

The study presents a novel method to generate large-scale synthetic datasets from GTA V for robotics, enabling scalable research without human supervision.

Findings

Synthetic GTA V data are qualitatively comparable to real data.

Synthetic data can effectively supplement or replace real-world data.

The approach facilitates scalable, cost-effective dataset creation for robotics.

Abstract

In computer vision, the development of robust algorithms capable of generalizing effectively in real-world scenarios more and more often requires large-scale datasets collected under diverse environmental conditions. However, acquiring such datasets is time-consuming, costly, and sometimes unfeasible. To address these limitations, the use of synthetic data has gained attention as a viable alternative, allowing researchers to generate vast amounts of data while simulating various environmental contexts in a controlled setting. In this study, we investigate the use of synthetic data in robotics and navigation, specifically focusing on Simultaneous Localization and Mapping (SLAM) and Visual Place Recognition (VPR). In particular, we introduce a synthetic dataset created using the virtual environment of the video game Grand Theft Auto V (GTA V), along with an algorithm designed to generate a VPR dataset, without human supervision. Through a series of experiments centered on SLAM and VPR, we demonstrate that synthetic data derived from GTA V are qualitatively comparable to real-world data. Furthermore, these synthetic data can complement or even substitute real-world data in these applications. This study sets the stage for the creation of large-scale synthetic datasets, offering a cost-effective and scalable solution for future research and development.