Real-Time 3D Vision-Language Embedding Mapping

Abstract: A metric-accurate semantic 3D representation is essential for many robotic tasks. This work proposes a simple, yet powerful, way to integrate the 2D embeddings of a Vision-LLM in a metric-accurate 3D representation at real-time. We combine a local embedding masking strategy, for a more distinct embedding distribution, with a confidence-weighted 3D integration for more reliable 3D embeddings. The resulting metric-accurate embedding representation is task-agnostic and can represent semantic concepts on a global multi-room, as well as on a local object-level. This enables a variety of interactive robotic applications that require the localisation of objects-of-interest via natural language. We evaluate our approach on a variety of real-world sequences and demonstrate that these strategies achieve a more accurate object-of-interest localisation while improving the runtime performance in order to meet our real-time constraints. We further demonstrate the versatility of our approach in a variety of interactive handheld, mobile robotics and manipulation tasks, requiring only raw image data.