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DRIVE-Nav: Directional Reasoning, Inspection, and Verification for Efficient Open-Vocabulary Navigation

Maoguo Gao, Zejun Zhu, Zhiming Sun, Zhengwei Ma, Longze Yuan, Zhongjing Ma, Zhigang Gao, Jinhui Zhang, Suli Zou

Abstract

Open-Vocabulary Object Navigation (OVON) requires an embodied agent to locate a language-specified target in unknown environments. Existing zero-shot methods often reason over dense frontier points under incomplete observations, causing unstable route selection, repeated revisits, and unnecessary action overhead. We present DRIVE-Nav, a structured framework that organizes exploration around persistent directions rather than raw frontiers. By inspecting encountered directions more completely and restricting subsequent decisions to still-relevant directions within a forward 240 degree view range, DRIVE-Nav reduces redundant revisits and improves path efficiency. The framework extracts and tracks directional candidates from weighted Fast Marching Method (FMM) paths, maintains representative views for semantic inspection, and combines vision-language-guided prompt enrichment with cross-frame verification to improve grounding reliability. Experiments on HM3D-OVON, HM3Dv2, and MP3D demonstrate strong overall performance and consistent efficiency gains. On HM3D-OVON, DRIVE-Nav achieves 50.2% SR and 32.6% SPL, improving the previous best method by 1.9% SR and 5.6% SPL. It also delivers the best SPL on HM3Dv2 and MP3D and transfers to a physical humanoid robot. Real-world deployment also demonstrates its effectiveness. Project page: https://coolmaoguo.github.io/drive-nav-page/

DRIVE-Nav: Directional Reasoning, Inspection, and Verification for Efficient Open-Vocabulary Navigation

Abstract

Open-Vocabulary Object Navigation (OVON) requires an embodied agent to locate a language-specified target in unknown environments. Existing zero-shot methods often reason over dense frontier points under incomplete observations, causing unstable route selection, repeated revisits, and unnecessary action overhead. We present DRIVE-Nav, a structured framework that organizes exploration around persistent directions rather than raw frontiers. By inspecting encountered directions more completely and restricting subsequent decisions to still-relevant directions within a forward 240 degree view range, DRIVE-Nav reduces redundant revisits and improves path efficiency. The framework extracts and tracks directional candidates from weighted Fast Marching Method (FMM) paths, maintains representative views for semantic inspection, and combines vision-language-guided prompt enrichment with cross-frame verification to improve grounding reliability. Experiments on HM3D-OVON, HM3Dv2, and MP3D demonstrate strong overall performance and consistent efficiency gains. On HM3D-OVON, DRIVE-Nav achieves 50.2% SR and 32.6% SPL, improving the previous best method by 1.9% SR and 5.6% SPL. It also delivers the best SPL on HM3Dv2 and MP3D and transfers to a physical humanoid robot. Real-world deployment also demonstrates its effectiveness. Project page: https://coolmaoguo.github.io/drive-nav-page/

Paper Structure

This paper contains 20 sections, 3 equations, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Zero-Shot Object Navigation
  4. LLM-Guided Navigation
  5. Problem formulation
  6. Method
  7. Directional Reasoning over Persistent Directions
  8. Frontier extraction
  9. Path-induced direction candidates
  10. Direction clustering and temporal tracking
  11. Visual summarization and semantic selection.
  12. Direction-Guided Prompt Enrichment
  13. Cross-frame Verification
  14. Experiments
  15. Experimental Setup
  16. ...and 5 more sections

Figures (4)

  • Figure 2: Comparison of exploration paradigms. Frontier-based exploration selects among frontier points without directly observing the unexplored regions beyond them, whereas direction-based exploration actively inspects junction information before making exploration decisions.
  • Figure 3: Overview of the proposed closed-loop navigation pipeline. RGB-D observations are converted into persistent exits for direction selection, prompt enhancement, and cross-frame target verification.
  • Figure 4: Directional abstraction during a representative exploration episode. As the agent reaches key decision points, local directional bearings are re-clustered. When adjacent angular gaps exceed the 45$^\circ$ threshold, new direction categories are identified. The agent then alternates between inspection and forward exploration.
  • Figure 5: Real-world deployment of DRIVE-Nav for finding an elevator. The sequence shows the full navigation episode, including $360^\circ$ initialization, successive $240^\circ$ direction decisions, cross-frame verification, and final approach to the target, with colored boxes indicating candidate directions in the same color and solid lines marking the selected direction.