MG-Nav: Dual-Scale Visual Navigation via Sparse Spatial Memory

TL;DR

MG-Nav introduces a dual-scale visual navigation framework utilizing a sparse spatial memory graph for global planning and local control, achieving state-of-the-art zero-shot performance in complex 3D environments.

Contribution

The paper proposes MG-Nav, a novel dual-scale navigation system combining global memory-guided planning with local obstacle-aware control, enhanced by a geometric feature alignment module.

Findings

Achieves state-of-the-art zero-shot navigation performance.

Robust under dynamic scene changes and unseen environments.

Effectively integrates global and local navigation strategies.

Abstract

We present MG-Nav (Memory-Guided Navigation), a dual-scale framework for zero-shot visual navigation that unifies global memory-guided planning with local geometry-enhanced control. At its core is the Sparse Spatial Memory Graph (SMG), a compact, region-centric memory where each node aggregates multi-view keyframe and object semantics, capturing both appearance and spatial structure while preserving viewpoint diversity. At the global level, the agent is localized on SMG and a goal-conditioned node path is planned via an image-to-instance hybrid retrieval, producing a sequence of reachable waypoints for long-horizon guidance. At the local level, a navigation foundation policy executes these waypoints in point-goal mode with obstacle-aware control, and switches to image-goal mode when navigating from the final node towards the visual target. To further enhance viewpoint alignment and goal recognition, we introduce VGGT-adapter, a lightweight geometric module built on the pre-trained VGGT model, which aligns observation and goal features in a shared 3D-aware space. MG-Nav operates global planning and local control at different frequencies, using periodic re-localization to correct errors. Experiments on HM3D Instance-Image-Goal and MP3D Image-Goal benchmarks demonstrate that MG-Nav achieves state-of-the-art zero-shot performance and remains robust under dynamic rearrangements and unseen scene conditions.