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 on-board RGB-D camera. 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. 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 applicability of our active scene reconstruction framework in the real world using an unmanned aerial vehicle.
机器人应用通常依赖场景重建来实现下游任务。在这项工作中，我们应对了使用车载RGB-D摄像头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，将高斯点云图与粗略体素图相结合，利用两种表示方式的优势：高斯点云在场景重建方面的高保真能力和体素图在空间建模方面的优势。我们框架的核心是一种有效的高斯点云置信度建模技术，用于识别重建不足的区域，同时利用体素图的空间信息来定位未探索区域并辅助无碰撞路径规划。通过在重建不足和未探索区域主动收集场景信息以更新地图，我们的方法在高斯点云重建结果上优于最先进的方法。此外，我们还通过使用无人机展示了我们主动场景重建框架在现实世界中的适用性。