Problem
Research questions and friction points this paper is trying to address.
spatio-temporal semantic representation
open-vocabulary perception
long-term environmental changes
identity drift
stale semantics
SuperMap: A Spatio-Temporal SLAM System for Visual-Language Navigation
🤖 AI Summary
为解决机器人导航中开放词汇感知与环境长期变化的融合问题，SuperMap提出了一种结合高频几何SLAM和异步开放词汇感知的4D时空映射框架。
📝 Abstract
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with a principled existence-and-label confidence update to maintain stable object identities and prune outdated map content under occlusions and scene changes.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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
4D Spatio-temporal SLAM
Consistency-driven Mapping Engine
Open-vocabulary Perception
Stable Object Identities
Scene-graph Representation
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