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Dual-Layer Semantic-Spatial Belief Mapping for Aerial Object Goal Navigation

Jianqiang Xiao, Xiang Deng, Yuexuan Sun, Yanjin Wu, Wenbiao Yan, Liqiang Nie

arXiv:2609.08164v1cs.ROcs.AI

TL;DR

Aerial ObjectNav must locate and approach described targets despite noisy, sparse, and transient VLM observations in unknown outdoor environments. AeroBelief converts these observations into persistent dual-layer spatial belief and stable coverage guidance, achieving the best reported overall SR, OSR, and SPL on UAV-ON among compared methods.

  • Problem

    Aerial ObjectNav lacks persistent, reliable spatial handling for noisy VLM judgments that can blur contextual plausibility with target-specific evidence.

    Source: §I · p. 1

  • Method

    AeroBelief combines target-conditioned conservative evidence qualification, dual-layer intuition–evidence mapping with evidence-gated fusion, and temporally committed egocentric regional guidance.

    Source: §I · p. 2

  • Results

    AeroBelief achieves the best reported overall SR, OSR, and SPL among compared methods on UAV-ON, reaching 21.61%, 35.57%, and 10.62, respectively.

    Source: D. Main Comparison · p. 7, §V · p. 9

  • Takeaways & Limitations

    The results support persistent semantic-spatial belief, conservative evidence qualification, and temporally stable regional guidance for aerial ObjectNav.

    Source: §V · p. 9

    Evaluation is limited to AirSim simulation, and sim-to-real transfer remains unvalidated; reliable target confirmation and stopping remain challenges.

    Source: §V · p. 9

Abstract

from arXiv · show

Aerial Object Goal Navigation (ObjectNav) requires an unmanned aerial vehicle (UAV) to locate a described target in an unknown outdoor environment using onboard visual observations. Vision-language models (VLMs) can interpret open-ended target descriptions and visual observations, but their frame-level outputs are often noisy, sparse, and spatially transient. We propose AeroBelief, a dual-layer semantic-spatial belief mapping framework that transforms transient VLM observations into persistent spatial guidance. It separates broad contextual plausibility from target-specific evidence: an intuition layer accumulates scene-level semantic cues for exploration, while an evidence layer preserves qualified target-specific observations for approach and confirmation. Evidence-gated fusion combines the two layers into spatial belief hotspots. We further introduce object-conditioned visual reasoning with conservative evidence qualification to improve observation reliability before spatial accumulation. In parallel, egocentric regional guidance converts quadtree coverage into UAV-centered, yaw-aligned directional proposals and stabilizes them through temporal commitment. Its regional scoring is independent of semantic belief values, maintaining exploration pressure and reducing repeated low-gain search. Experiments on the UAV-ON benchmark show that AeroBelief achieves the best reported overall SR, OSR, and SPL among the compared methods, reaching 21.61%, 35.57%, and 10.62, respectively. These results support the effectiveness of persistent semantic-spatial belief, conservative evidence qualification, and temporally stable regional guidance for aerial ObjectNav.

I. Introduction

AeroBelief addresses aerial ObjectNav challenges caused by transient VLM judgments, sparse target visibility, and large unknown outdoor spaces. It combines persistent dual-layer semantic belief with conservative evidence qualification and stable regional exploration guidance.

  • Aerial ObjectNav requires searching and approaching a described target in large, partially unknown outdoor environments with viewpoint changes, occlusion, and sparse visibility.Source: §I · p. 1
  • Frame-level VLM judgments may lose spatial persistence and conflate contextual plausibility with target-specific evidence, producing misleading hotspots and repeated irrelevant searches.Source: §I · p. 1
  • AeroBelief preserves contextual intuition and qualified target evidence in separate layers, then fuses them into spatial belief hotspots for planner-level decisions.Source: §I · p. 2
  • Conservative evidence qualification uses target-conditioned reasoning to restrict insufficiently supported target claims before persistent spatial accumulation.Source: §I · pp. 1–2
  • Egocentric regional guidance converts quadtree coverage into yaw-aligned directional bins and uses temporal commitment to reduce oscillation and redundant revisitation.Source: §I · p. 2

C. Semantic Mapping for Active Search

The framework interprets target-conditioned observations and projects them into persistent aerial search representations while preserving coverage-oriented exploration. Success requires explicit stopping within a predefined target radius, not merely entering the target vicinity.

  • Aerial ObjectNav uses an unknown outdoor environment, a natural-language target specification, and onboard visual observations without providing the target position.Source: A. Task Formulation · p. 3
  • Success requires an explicit stop action within a predefined success radius, distinguishing successful stopping from merely reaching the target vicinity.Source: A. Task Formulation · p. 3
  • The framework projects semantic signals into a shared search grid while independently using egocentric, yaw-aligned coverage guidance stabilized by temporal commitment.Source: B. System Overview · p. 3
  • Object-conditioned reasoning builds a target cue from the specification and initial multi-view observations to guide interpretation rather than action selection.Source: C. Object-Conditioned Visual Reasoning · p. 3, 1) Target-Conditioned Semantic… · p. 3
  • Structured semantic judgments distinguish exact and strong semantic matches from weak contextual matches, which can guide exploration without confirming target presence.Source: 1) Target-Conditioned Semantic… · pp. 3–4

2) Conservative Evidence Qualification:

Conservative evidence qualification revises target claims when visual support is insufficient, preserving useful contextual guidance while preventing unsupported claims from entering the evidence layer.

D. Dual-Layer Semantic-Spatial Belief Mapping

AeroBelief maintains complementary intuition and evidence maps over a shared aerial grid, projecting qualified observations spatially and updating the layers with different persistence rules.

2) Evidence-Gated Fusion and Hotspot Interface:

Evidence-gated fusion combines contextual intuition with qualified target-specific evidence, increasing the evidence contribution as reliable observations accumulate. The fused map is then compressed into directional hotspots for planner use.

E. Egocentric Regional Guidance

Egocentric regional guidance turns quadtree coverage into UAV-centered directional proposals whose scores remain independent of semantic belief. Temporal commitment stabilizes these proposals while allowing the planner to weigh them against semantic hotspots and execution constraints.

  • Regional scoring evaluates exploration opportunity, reachability, and revisit-aware efficiency independently of semantic belief values.The stream complements fine-grained semantic belief when reliable evidence is sparse.Source: E. Egocentric Regional Guidance · p. 6, 1) Egocentric Regional Proposal · p. 6
  • Quadtree coverage is transformed into a 5 × 5 body-frame grid reconstructed from the UAV’s current pose and yaw.Rows follow the forward–backward axis, columns follow the left–right axis, and the center contains the UAV.Source: 1) Egocentric Regional Proposal · p. 6
  • Each bin represents an egocentric directional region rather than a persistent world-space partition.The same bin index can correspond to different world locations as the UAV moves or rotates.Source: 1) Egocentric Regional Proposal · p. 6
  • The instantaneous candidate is recomputed from geometrically valid bins as the most promising current exploration direction.Source: 1) Egocentric Regional Proposal · p. 6
  • Temporal commitment retains a useful direction or updates it when stronger opportunities, low progress, obstacles, or repeated visitation make the current choice stale.Minimum duration and switching hysteresis reduce short-term oscillation.Source: 1) Egocentric Regional Proposal · p. 6
  • The committed cue expresses an exploration preference, while the planner may depart from it when stronger target evidence or motion constraints support another action.The cue is not an exact metric waypoint or a directly prescribed action.Source: 1) Egocentric Regional Proposal · pp. 6–7

F. Planner-Level Integration

A shared planner and execution backbone combines semantic observations, belief hotspots, regional guidance, pose–action history, and feasible actions for high-level proposals. On UAV-ON, AeroBelief achieved the best reported overall SR, OSR, and SPL among compared methods, with size-dependent behavior and representative episodes showing progressive belief concentration.

  • The planner receives semantic observations, belief hotspots, committed regional guidance, pose–action history, and feasible-action information to produce a high-level action proposal.Source: F. Planner-Level Integration · p. 7
  • The shared execution layer handles stop verification, collision avoidance, boundary and altitude constraints, and fallback recovery across variants.Source: F. Planner-Level Integration · p. 7
  • AeroBelief achieves the best reported overall SR, OSR, and SPL, reaching 21.61%, 35.57%, and 10.62, respectively.It improves over the strongest previously reported result by 2.11 percentage points in SR, 6.27 in OSR, and 0.48 in SPL.Source: D. Main Comparison · p. 7
  • Small and medium targets rank first across all three metrics, while large targets achieve the second-highest SR and SPL but lower OSR than several prior methods.Source: D. Main Comparison · p. 7
  • The largest size-specific OSR–SR gap is 18.44 percentage points for small targets, indicating frequent target-vicinity discovery without consistently successful stopping.Source: D. Main Comparison · p. 7
  • Representative successful episodes show intuition maintaining broad contextual belief before qualified evidence concentrates fused belief around the target.The UAV may continue regional search, adjust viewpoint or altitude, and reject ambiguous candidates before sufficient evidence accumulates.Source: D. Main Comparison · p. 7

E. Ablation Study

The ablation study shows that combining semantic-spatial belief mapping, object-conditioned reasoning, and regional guidance yields the strongest navigation performance, while persistent evidence and temporal commitment provide additional gains.

  • AeroBelief achieves the highest SR, OSR, and SPL and the lowest collision rate across the ablation variants.Removing any complete proposed pathway causes a larger degradation than removing only its paired subcomponent.Source: E. Ablation Study · pp. 7–8
  • The results support semantic-spatial belief mapping, object-conditioned reasoning, and regional guidance, with evidence qualification and temporal commitment adding further gains.Source: E. Ablation Study · p. 8
  • 25.00% SR versus 11.00% for Vanilla accompanies a collision-termination reduction from 39.50% to 28.00%.Source: E. Ablation Study · p. 9
  • Removing the evidence layer increases MaxCov but lowers SR and SPL, showing that broader coverage alone does not ensure target confirmation.Persistent target-specific evidence helps convert exploration into successful approach and stopping.Source: E. Ablation Study · pp. 8–9

Appendix A Shared Autonomous UAV Backbone

The shared UAV backbone converts multi-view RGB-D observations and state information into structured coverage and navigation inputs. Its incremental quadtree represents geometric coverage, while common execution and verification procedures constrain actions consistently across variants.

B. Coverage-Guided Regional Reasoning

Coverage-guided regional reasoning converts quadtree leaves into UAV-centered directional bins and selects exploration proposals using geometry and coverage rather than semantic belief. Temporal commitment stabilizes these proposals across steps.

  • Quadtree leaves are transformed into the UAV body frame and aggregated into a 5 × 5 yaw-aligned grid of relative directional bins.Rows follow the forward–backward axis, columns follow the left–right axis, and the center contains the UAV.Source: B. Coverage-Guided Regional Reasoning · p. 12
  • Each bin receives a coverage-oriented score based on exploration opportunity, reachability, and revisit-aware spatial penalties.The score considers unknown area, frontiers, heading alignment, travel distance, boundaries, spatial support, and visitation history, but not semantic belief.Source: B. Coverage-Guided Regional Reasoning · p. 12
  • The instantaneous candidate is selected from geometrically valid bins under the current search boundary.Source: B. Coverage-Guided Regional Reasoning · p. 12
  • Temporal commitment retains a useful direction until a stronger candidate appears or the current one becomes stale.Minimum commitment duration and switching hysteresis reduce short-term oscillation.Source: B. Coverage-Guided Regional Reasoning · p. 13
  • The committed relative bin is reinterpreted in the current body-frame grid after movement or rotation and supplied as coarse guidance rather than an exact waypoint.Source: B. Coverage-Guided Regional Reasoning · p. 13

Appendix C Prompt Design and Structured Reasoning Interface

The prompt design uses target-conditioned cues and structured per-view reasoning to support conservative, non-action-oriented visual interpretation. The planner receives organized summaries and rendered maps instead of raw camera data.

A. Target-Conditioned Visual Reasoning

A. Structured Reasoning Interface

  • The navigation planner receives rendered spatial maps and organized textual context rather than raw RGB-D observations.Source: Figure 7 · p. 13

A. Target-Conditioned Visual Reasoning

B. Structured Planner Interface

The structured planner combines fused semantic belief and quadtree coverage with organized textual context, then passes high-level proposals through shared execution safeguards. Failure analysis shows that incomplete exploration and termination miscalibration remain important error sources, especially for large targets.

Structured Planner Interface

Failure Mode Analysis

Target-Size Sensitivity

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