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.

翻译：本文提出AG-SLAM，这是首个利用三维高斯溅射（3DGS）进行在线场景重建的主动SLAM系统。近年来，包括3DGS在内的辐射场场景表示已广泛应用于SLAM与探索领域，但针对机器人探索的主动轨迹规划仍属空白。尤其值得注意的是，许多探索方法假设定位精确，因而未能缓解构建轨迹的显著风险——这种轨迹对SLAM系统的运行极具挑战性，可能导致相机跟踪失败，进而在现实机器人应用中引发系统故障。本方法通过费舍尔信息矩阵平衡双重目标：在最大化环境信息增益的同时，最小化定位误差代价。基于Gibson与Habitat-Matterport 3D数据集的实验表明，所提方法取得了当前最优的性能。