发表机构
The Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出SuperMap，一种结合几何SLAM与开放词汇感知的4D时空建图框架，用于视觉-语言导航，可稳定维护目标身份，经基准与真实机器人验证后开源。
AI 中文摘要
人类环境中的机器人导航需要一种时空语义表示，以协调开放词汇感知与长期环境变化。虽然基础模型提供了强大的零样本识别能力，但其预测是间歇性且依赖视角的，将其直接集成到建图流水线会随时间导致身份漂移和过时语义。本文提出SuperMap，这是一种用于语言引导导航的4D时空建图框架，将高频几何SLAM与异步开放词汇感知相结合。核心贡献是一致性驱动的建图引擎，它结合3D感知的实例关联/重激活，以及基于原则的存在性与标签置信度更新，以在遮挡和场景变化下维持稳定的目标身份并修剪过时的地图内容。SuperMap生成可查询的4D场景图表示，支持对目标语义、关系的组合查询，可自然与视觉-语言模型交互。我们在基准测试和真实机器人（包括存在外观变化/消失和重定位的动态场景）上验证了SuperMap，还提供了 ablation 研究和运行时分析。我们将整个系统作为开源发布，为社区提供可部署的开放词汇时空建图基线。项目网站：this http URL
英文摘要
Robotic navigation in human environments requires a spatio-temporal semantic representation that can rec- oncile open-vocabulary perception with long-term environmental changes. While foundation models provide strong zero-shot recognition, their predictions are intermittent and view-dependent, and naively integrating them into mapping pipelines leads to identity drift and stale semantics over time. We present SuperMap, a 4D spatio-temporal mapping framework for language-guided navigation that integrates high-frequency geometric SLAM with asynchronous open-vocabulary perception. Our core contribution is a consistency-driven mapping engine that combines 3D-aware instance association/re-activation with a principled existence-and-label confidence update to maintain stable object identities and prune outdated map content under occlusions and scene changes. SuperMap produces a queryable 4D scene-graph representation that interfaces naturally with Vision-Language Models by supporting compositional queries over object semantics, relations, We demonstrate SuperMap on benchmarks and real robots, including dynamic scenes with appearance/disappearance and relocation, and provide ablations and runtime analysis. We release the full system as open-source to provide the community with a deployable baseline for open-vocabulary spatio-temporal mapping. Project website: superodometry.com/supermap.
Journal refProceedings of Robotics: Science and Systems (RSS 2026)
