3Dガウシアンスプラッティングを活用した大規模複雑屋内環境の視覚的自己位置推定
3D Gaussian Splatting aided Localization for Large and Complex Indoor-Environments
3Dガウシアンスプラッティングで生成した地図からレンダリング画像を参照データに追加し、視覚的自己位置推定の精度と信頼性を向上させる手法を提案した。
著者: Vincent Ress, Jonas Meyer, Wei Zhang, David Skuddis, Uwe Soergel, Norbert Haala
分類: cs.CV, cs.RO
原文アブストラクト
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.
