We present SIREN for registration of multi-robot Gaussian Splatting (GSplat) maps, with zero access to camera poses, images, and inter-map transforms for initialization or fusion of local submaps. To realize these capabilities, SIREN harnesses the versatility and robustness of semantics in three critical ways to derive a rigorous registration pipeline for multi-robot GSplat maps. First, SIREN utilizes semantics to identify feature-rich regions of the local maps where the registration problem is better posed, eliminating the need for any initialization which is generally required in prior work. Second, SIREN identifies candidate correspondences between Gaussians in the local maps using robust semantic features, constituting the foundation for robust geometric optimization, coarsely aligning 3D Gaussian primitives extracted from the local maps. Third, this key step enables subsequent photometric refinement of the transformation between the submaps, where SIREN leverages novel-view synthesis in GSplat maps along with a semantics-based image filter to compute a high-accuracy non-rigid transformation for the generation of a high-fidelity fused map. We demonstrate the superior performance of SIREN compared to competing baselines across a range of real-world datasets, and in particular, across the most widely-used robot hardware platforms, including a manipulator, drone, and quadruped. In our experiments, SIREN achieves about 90x smaller rotation errors, 300x smaller translation errors, and 44x smaller scale errors in the most challenging scenes, where competing methods struggle. We will release the code and provide a link to the project page after the review process.
我们提出了SIREN，用于多机器人高斯溅射（GSplat）地图的配准，且无需访问相机位姿、图像或本地子图的地图间转换进行初始化或融合。为实现这些功能，SIREN利用语义的多样性和鲁棒性，通过三种关键方式推导出多机器人GSplat地图的严格配准流程。首先，SIREN利用语义识别本地地图中的特征丰富区域，这些区域使得配准问题更易解决，从而消除了先前工作中通常需要的初始化步骤。其次，SIREN通过鲁棒的语义特征识别本地地图中高斯之间的候选对应关系，为稳健的几何优化奠定了基础，粗略地对从本地地图提取的3D高斯原语进行对齐。第三，这一步骤使得后续的子图之间的光度细化成为可能，SIREN在GSplat地图中利用新视角合成和基于语义的图像滤波器来计算高精度的非刚性变换，以生成高保真的融合地图。我们在多个真实世界数据集上展示了SIREN相较于竞争基线的优越性能，特别是在最广泛使用的机器人硬件平台上，包括机械臂、无人机和四足机器人。在我们的实验中，SIREN在最具挑战性的场景中实现了约90倍更小的旋转误差、300倍更小的平移误差和44倍更小的尺度误差，而竞争方法则难以应对这些场景。我们将在审稿过程后发布代码并提供项目页面链接。