We present AG-SLAM, the first active SLAM system utilizing 3D Gaussian Splatting (3DGS) for online scene reconstruction. In recent years, radiance field scene representations, including 3DGS have been widely used in SLAM and exploration, but actively planning trajectories for robotic exploration is still unvisited. In particular, many exploration methods assume precise localization and thus do not mitigate the significant risk of constructing a trajectory, which is difficult for a SLAM system to operate on. This can cause camera tracking failure and lead to failures in real-world robotic applications. Our method leverages Fisher Information to balance the dual objectives of maximizing the information gain for the environment while minimizing the cost of localization errors. Experiments conducted on the Gibson and Habitat-Matterport 3D datasets demonstrate state-of-the-art results of the proposed method.
我们提出了 AG-SLAM，这是首个利用三维高斯喷涂 (3D Gaussian Splatting, 3DGS) 进行在线场景重建的主动 SLAM 系统。近年来，辐射场场景表示（包括 3DGS）在 SLAM 和环境探索中得到了广泛应用，但主动规划机器人探索的轨迹仍未被深入研究。尤其是，许多探索方法假设精确定位，从而未能解决构建难以用于 SLAM 系统的轨迹的显著风险。这可能导致摄像机跟踪失败，从而影响现实中的机器人应用。我们的方法利用费舍尔信息，在最大化环境信息增益和最小化定位误差成本的双重目标间实现平衡。基于 Gibson 和 Habitat-Matterport 3D 数据集的实验结果表明，所提出的方法达到了最新的技术水平。