SIREN: Semantic, Initialization-Free Registration of Multi-Robot Gaussian Splatting Maps

Olao Shorinwa, Jiankai Sun, Mac Schwager, Anirudha Majumdar

Keywords: Multi-Robot Radiance Field Mapping, Gaussian Splatting

TL;DR: We introduce an algorithm for registering multi-robot Gaussian Splatting maps, leveraging rich robust semantic distillation and feature matching for high-accuracy map fusion with no initialization.

Abstract:

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 fact, in the most challenging scenes
where accurate feature matching is extremely challenging,
SIREN achieves about 90x
smaller rotation errors, 300x smaller translation errors, and
44x smaller scale errors, compared to
competing methods.
We will release the code and provide
a link to the project page after the review process.

Submission Number: 682