OVAL introduces an open-vocabulary memory model with structured descriptors and multi-value frontier scoring to enable efficient lifelong object goal navigation in unseen settings.
Vlfm: Vision- language frontier maps for zero-shot semantic navigation,
4 Pith papers cite this work. Polarity classification is still indexing.
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cs.RO 4years
2026 4representative citing papers
ReMemNav improves zero-shot object navigation success and efficiency by integrating episodic memory and rethinking with VLMs, achieving SR/SPL gains of 1.7%/7.0% on HM3D v0.1, 18.2%/11.1% on HM3D v0.2, and 8.7%/7.9% on MP3D.
A map-free localization method stores posed RGB-D keyframes, retrieves and re-ranks them with a VLM, then fuses sparse depth for on-demand 3D target estimates, matching reconstruction-based performance on navigation benchmarks with far lower build cost.
IGV-RRT improves object goal navigation in dynamic indoor environments by combining uncertainty-aware priors from 3D scene graphs with online VLM observations in a real-time tree planner.
citing papers explorer
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OVAL: Open-Vocabulary Augmented Memory Model for Lifelong Object Goal Navigation
OVAL introduces an open-vocabulary memory model with structured descriptors and multi-value frontier scoring to enable efficient lifelong object goal navigation in unseen settings.
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ReMemNav: A Rethinking and Memory-Augmented Framework for Zero-Shot Object Navigation
ReMemNav improves zero-shot object navigation success and efficiency by integrating episodic memory and rethinking with VLMs, achieving SR/SPL gains of 1.7%/7.0% on HM3D v0.1, 18.2%/11.1% on HM3D v0.2, and 8.7%/7.9% on MP3D.
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Memory Over Maps: 3D Object Localization Without Reconstruction
A map-free localization method stores posed RGB-D keyframes, retrieves and re-ranks them with a VLM, then fuses sparse depth for on-demand 3D target estimates, matching reconstruction-based performance on navigation benchmarks with far lower build cost.
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IGV-RRT: Prior-Real-Time Observation Fusion for Active Object Search in Changing Environments
IGV-RRT improves object goal navigation in dynamic indoor environments by combining uncertainty-aware priors from 3D scene graphs with online VLM observations in a real-time tree planner.