LCGNav: Local Candidate-Aware Geometric Enhancement for General Topological Planning in Vision-Language Navigation
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Abstract (arXiv)
Online topological planning has become an effective paradigm for Vision-Language Navigation in Continuous Environments (VLN-CE), but existing methods still suffer from two limitations: redundant local depth information and weakened focus on current frontier candidates as the topological graph grows. To address this, we propose LCGNav, a modular local geometric enhancement framework for topological VLN. LCGNav explicitly converts candidate depth views into 3D point clouds and applies physical truncation based on the agent's reachable range, enabling more compact local geometric modeling. It further introduces a dimension-preserving local fusion strategy with transient state degradation, so that geometric enhancement is applied only to the currently relevant ghost nodes without changing the original planner interface. Experiments on R2R-CE and RxR-CE show that LCGNav serves as an effective cross-architecture enhancement module, consistently improving multiple key metrics of representative online topological baselines with low additional training cost. When integrated with ETP-R1, LCGNav achieves the best performance among the compared online topological methods on the val-unseen splits of the R2R-CE and RxR-CE benchmarks. The code is available at https://github.com/shannanshouyin/LCGNav.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 24 references that are in the map. First posted on arXiv 9 May 2026.
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References · 22
- 2019Habitat: A Platform for Embodied AI Researchcited by 1,163
- 2017Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environmentscited by 1,013
- 2020Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environmentscited by 429
- 2021History Aware Multimodal Transformer for Vision-and-Language Navigationcited by 268
- 2024NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigationcited by 222
- 2022Think Global, Act Local: Dual-scale Graph Transformer for Vision-and-Language Navigationcited by 187
- 2023ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environmentscited by 165
- 2024Uni-NaVid: A Video-based Vision-Language-Action Model for Unifying Embodied Navigation Taskscited by 146
- 2022Bridging the Gap Between Learning in Discrete and Continuous Environments for Vision-and-Language Navigationcited by 127
- 2022BEVBert: Multimodal Map Pre-training for Language-guided Navigationcited by 117
- 2023GridMM: Grid Memory Map for Vision-and-Language Navigationcited by 117
- 2020Topological Planning with Transformers for Vision-and-Language Navigationcited by 115
10 more
- 2025StreamVLN: Streaming Vision-and-Language Navigation via SlowFast Context Modelingcited by 107
- 2022Cross-modal Map Learning for Vision and Language Navigationcited by 91
- 2022Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigationcited by 86
- 2022Sim-2-Sim Transfer for Vision-and-Language Navigation in Continuous Environmentscited by 80
- 2025JanusVLN: Decoupling Semantics and Spatiality with Dual Implicit Memory for Vision-Language Navigationcited by 80
- 2025MapNav: A Novel Memory Representation via Annotated Semantic Maps for Vision-and-Language Navigationcited by 55
- 2023Safe-VLN: Collision Avoidance for Vision-and-Language Navigation of Autonomous Robots Operating in Continuous Environmentscited by 22
- 2025ETP-R1: Evolving Topological Planning with Reinforcement Fine-tuning for Vision-Language Navigation in Continuous Environmentscited by 4
- 2026Nipping the Drift in the Bud: Retrospective Rectification for Robust Vision-Language Navigationcited by 3
- 2026Dynamic Topology Awareness: Breaking the Granularity Rigidity in Vision-Language Navigationcited by 1
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