ActiveGS：基于高斯泼溅的主动场景重建
ActiveGS: Active Scene Reconstruction Using Gaussian Splatting
AI总结：
本文提出ActiveGS框架，将高斯泼溅地图与体素地图结合，通过置信度建模识别重建不足区域并主动采集信息，在RGB-D相机移动平台上实现了优于现有方法的主动场景重建，并在无人机上验证了实用性。
摘要：
机器人应用通常依赖场景重建来实现下游任务。在本工作中，我们致力于解决在移动平台上使用RGB-D相机主动构建未知场景精确地图的挑战。我们提出了一种混合地图表示方法，将高斯泼溅地图与粗略体素地图相结合，充分利用两种表示的优势：高斯泼溅的高保真场景重建能力以及体素地图的空间建模优势。我们框架的核心是一种有效的高斯泼溅地图置信度建模技术，用于识别重建不足的区域，同时利用体素地图的空间信息来瞄准未探索区域并辅助无碰撞路径规划。通过主动收集重建不足和未探索区域的场景信息以更新地图，与最先进的方法相比，我们的方法取得了更优的高斯泼溅重建结果。此外，我们使用无人飞行器展示了该框架在真实世界中的适用性。
英文摘要：
Robotics applications often rely on scene reconstructions to enable downstream tasks. In this work, we tackle the challenge of actively building an accurate map of an unknown scene using an RGB-D camera on a mobile platform. We propose a hybrid map representation that combines a Gaussian splatting map with a coarse voxel map, leveraging the strengths of both representations: the high-fidelity scene reconstruction capabilities of Gaussian splatting and the spatial modelling strengths of the voxel map. At the core of our framework is an effective confidence modelling technique for the Gaussian splatting map to identify under-reconstructed areas, while utilising spatial information from the voxel map to target unexplored areas and assist in collision-free path planning. By actively collecting scene information in under-reconstructed and unexplored areas for map updates, our approach achieves superior Gaussian splatting reconstruction results compared to state-of-the-art approaches. Additionally, we demonstrate the real-world applicability of our framework using an unmanned aerial vehicle.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
