用游戏GTA V生成机器人导航数据，效果接近真实数据。
From Gaming to Research: GTA V for Synthetic Data Generation for Robotics and Navigations
- 用GTA V虚拟环境自动构建合成数据集，无需人工标注。
- 合成数据在SLAM和视觉定位任务中表现接近真实数据。
- 适合需要大量训练数据的机器人与导航研究者使用。
在计算机视觉中，开发能在真实场景中有效泛化的鲁棒算法越来越依赖大规模、多样环境下的数据集。然而，获取此类数据耗时、昂贵且有时不可行。为此，合成数据成为可行替代方案，可在受控环境中生成海量数据并模拟多种环境条件。本研究聚焦机器人与导航中的同时定位与地图构建（SLAM）及视觉地点识别（VPR），提出利用视频游戏《侠盗猎车：圣安地列斯》（GTA V）的虚拟环境创建合成数据集，并设计了一种无需人工监督的VPR数据生成算法。通过一系列针对SLAM和VPR的实验，我们证明来自GTA V的合成数据在质量上可与真实数据相媲美，且能有效补充甚至替代真实数据。该研究为构建大规模合成数据集奠定基础，提供了一种成本低、可扩展的未来研究与开发解决方案。
原文摘要 · Abstract (English)
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.
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