ActiveGS: Active Scene Reconstruction Using Gaussian Splatting
This addresses the challenge of scene reconstruction for robotics applications, enabling downstream tasks, but appears incremental as it builds on existing Gaussian splatting and voxel mapping techniques.
The paper tackles the problem of actively building an accurate scene map for robotics using an RGB-D camera by proposing a hybrid representation combining Gaussian splatting and voxel maps, achieving superior reconstruction results compared to state-of-the-art approaches.
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.