Dynamic Topology Awareness: Breaking the Granularity Rigidity in Vision-Language Navigation

TL;DR

DGNav introduces a dynamic, context-aware topological mapping framework for vision-language navigation, enabling adaptive graph construction and connectivity to improve navigation accuracy and efficiency in complex environments.

Contribution

The paper presents DGNav, a novel framework that dynamically adjusts topological map density and connectivity based on environmental context, addressing the rigidity of fixed thresholds in existing methods.

Findings

Outperforms existing methods on R2R-CE and RxR-CE benchmarks.

Achieves better navigation efficiency and safety through adaptive graph construction.

Demonstrates strong generalization across diverse environments.

Abstract

Vision-Language Navigation in Continuous Environments (VLN-CE) presents a core challenge: grounding high-level linguistic instructions into precise, safe, and long-horizon spatial actions. Explicit topological maps have proven to be a vital solution for providing robust spatial memory in such tasks. However, existing topological planning methods suffer from a "Granularity Rigidity" problem. Specifically, these methods typically rely on fixed geometric thresholds to sample nodes, which fails to adapt to varying environmental complexities. This rigidity leads to a critical mismatch: the model tends to over-sample in simple areas, causing computational redundancy, while under-sampling in high-uncertainty regions, increasing collision risks and compromising precision. To address this, we propose DGNav, a framework for Dynamic Topological Navigation, introducing a context-aware mechanism to modulate map density and connectivity on-the-fly. Our approach comprises two core innovations: (1) A Scene-Aware Adaptive Strategy that dynamically modulates graph construction thresholds based on the dispersion of predicted waypoints, enabling "densification on demand" in challenging environments; (2) A Dynamic Graph Transformer that reconstructs graph connectivity by fusing visual, linguistic, and geometric cues into dynamic edge weights, enabling the agent to filter out topological noise and enhancing instruction adherence. Extensive experiments on the R2R-CE and RxR-CE benchmarks demonstrate DGNav exhibits superior navigation performance and strong generalization capabilities. Furthermore, ablation studies confirm that our framework achieves an optimal trade-off between navigation efficiency and safe exploration. The code is available at https://github.com/shannanshouyin/DGNav.