用高斯点阵主动建图，提升未知场景重建精度
ActiveGS: Active Scene Reconstruction Using Gaussian Splatting
- 结合高斯点阵与体素地图，兼顾细节与空间结构
- 通过置信度识别重建不足区域，主动采集信息
- 适合无人机等移动平台的实时精准建图
机器人应用常依赖场景重建完成后续任务。本文针对移动平台搭载RGB-D相机时主动构建未知场景精确地图的挑战，提出一种混合地图表示：将高斯点阵地图与粗粒度体素地图结合，融合高斯点阵的高保真重建能力与体素地图的空间建模优势。核心是为高斯点阵地图设计有效的置信度建模技术，识别重建不充分区域；同时利用体素地图的空间信息定位未探索区域，并辅助实现无碰撞路径规划。通过主动在重建不足和未探索区域采集数据以更新地图，本方法在高斯点阵重建精度上优于现有最先进方法。此外，我们在无人飞行器上验证了该框架的实际可行性。
原文摘要 · Abstract (English)
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.
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