用3D高斯点云渲染图像，提升复杂室内环境定位精度
3D Gaussian Splatting aided Localization for Large and Complex Indoor-Environments
- 用3DGS构建地图并随机采样生成渲染图像作为参考数据
- 在工业级大场景中，定位准确率显著优于传统方法
- 适合需要高精度定位的机器人导航与数字孪生应用
视觉定位领域已研究数十年，并在诸多场景中得到应用。尽管进展显著，现有方法在复杂环境中仍表现不佳。本文提出通过添加从3D高斯点云（3DGS）渲染的图像来显著提升经典视觉定位方法的精度与可靠性。具体而言，首先采用现代视觉SLAM技术构建基于3DGS的地图，生成参考数据；随后，在随机采样的位姿下渲染图像并融入参考数据。实验表明，该策略可显著提升几何基定位与场景坐标回归（SCR）方法的性能。我们在大型工业环境中进行了全面评估，分析了引入额外渲染视图对定位性能的影响。
原文摘要 · Abstract (English)
The field of visual localization has been researched for several decades and has meanwhile found many practical applications. Despite the strong progress in this field, there are still challenging situations in which established methods fail. We present an approach to significantly improve the accuracy and reliability of established visual localization methods by adding rendered images. In detail, we first use a modern visual SLAM approach that provides a 3D Gaussian Splatting (3DGS) based map to create reference data. We demonstrate that enriching reference data with images rendered from 3DGS at randomly sampled poses significantly improves the performance of both geometry-based visual localization and Scene Coordinate Regression (SCR) methods. Through comprehensive evaluation in a large industrial environment, we analyze the performance impact of incorporating these additional rendered views.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。