Abstract
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.
Abstract (translated)
我们提出了AG-SLAM，这是首个利用3D高斯点云（3DGS）进行在线场景重建的主动SLAM系统。近年来，辐射场场景表示方法，包括3DGS，在SLAM和探索中得到了广泛应用，但为机器人探索积极规划轨迹仍然是未被涉足的领域。特别是，许多探索方法假设了精确的定位，并没有减轻构建对SLAM系统来说难以操作的轨迹所带来的显著风险。这可能导致相机追踪失败并导致现实世界中的机器人应用失败。我们的方法利用费舍尔信息来平衡最大化环境信息增益与最小化定位误差成本的双重目标。在Gibson和Habitat-Matterport 3D数据集上进行的实验显示了所提方法处于当前技术水平领先地位的结果。
URL
https://arxiv.org/abs/2410.17422
