TravExplorer: Cross-Floor Embodied Exploration via Traversability-Aware 3-D Planning

• URL: http://arxiv.org/abs/2605.19958v1
• Date: Tue, 19 May 2026 15:11:29 GMT
• Title: TravExplorer: Cross-Floor Embodied Exploration via Traversability-Aware 3-D Planning
• Abstract summary: This article presents TravExplorer, a cross-floor embodied exploration framework.<n>It couples zero-shot semantic guidance with traversability-aware 3-D planning.<n>Fifty real-world trials on a Unitree Go2 further validate open-vocabulary target search across single-floor and cross-floor indoor environments.
• Score: 17.54520943622758
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Zero-shot Object Navigation (ZSON) has shown promise for open-vocabulary target search in unseen environments, yet most existing systems remain tied to planar representations and single-floor assumptions. These assumptions become inadequate in real buildings, where navigation involves floors, stairs, landings, and vertically overlapping spaces. This article presents TravExplorer, a cross-floor embodied exploration framework that couples zero-shot semantic guidance with traversability-aware 3-D planning. TravExplorer maintains a unified volumetric map that distinguishes occupied structures from robot-reachable support surfaces and extracts traversable frontiers from connected support surfaces, including floors, stairs, and landings. A FOV-aware active perception strategy further resolves incomplete observations during cross-floor traversal. To reduce semantic-reasoning latency, a lightweight guidance module aligns a probabilistic instance map from online open-vocabulary segmentation with a spatial value map from fast image-to-text matching. Based on these geometric and semantic memories, a hierarchical planner performs target-aware frontier touring over object hypotheses, traversable frontiers, and stair landmarks, and generates executable cross-floor motions through foothold-guided 3-D search and vertically constrained local trajectory optimization. Experiments over 4,195 simulated episodes on HM3D and MP3D demonstrate consistent advantages over representative ObjectNav baselines. Fifty real-world trials on a Unitree Go2 further validate open-vocabulary target search across single-floor and cross-floor indoor environments without prior maps or human intervention. The code will be released at https://github.com/wuyi2121/TravExplorer.

Related papers

• Flying to Image-Specified Objects: 3D Quadrotor Navigation via Cross-Graph Memory and Viewpoint Planning [6.546667407424349]
  Instance-Specific Image-Goal Navigation (InstanceImageNav) requires a robot to navigate toward the exact object instance depicted in a query image.<n>We propose a hierarchical navigation framework for quadrotor InstanceImageNav that separates high-level decision making from low-level motion execution.
  arXiv  Detail & Related papers  (2026-06-29T07:54:24Z)
• SCAN-Planner: Spatial Collision-Aware Local Planning for Route-Guided Long-Range Quadruped Navigation [9.033597184884746]
  SCAN-Planner is a spatial collision-aware local planning framework for long-range quadruped navigation.<n>A yaw-aware twin-cylinder footprint is used to model the elongated robot body.<n>For large-scale deployment, a robot-centric sliding map with boundary fallback provides high-resolution local collision checking.
  arXiv  Detail & Related papers  (2026-06-17T19:55:09Z)
• Multi-Floor Exploration for Ground Robots via an Incremental Reachable Graph and Structural Priors [19.688434395072914]
  This letter presents a multi-floor exploration framework based on an incremental reachable graph.<n>Built as a sparse graph over reachable support surfaces, the graph preserves potentially valid connectivity.<n>In simulation, the proposed method demonstrates improved exploration efficiency and mapping completeness compared to evaluated baselines.
  arXiv  Detail & Related papers  (2026-05-22T08:13:19Z)
• SpatialFly: Geometry-Guided Representation Alignment for UAV Vision-and-Language Navigation in Urban Environments [49.966170814478915]
  We propose a geometry-guided spatial representation framework for UAV VLN.<n>Running on RGB observations without explicit 3D reconstruction, SpatialFly introduces a geometry-guided 2D representation alignment mechanism.<n> Experimental results show that SpatialFly consistently outperforms state-of-the-art UAV VLN baselines across both seen and unseen environments.
  arXiv  Detail & Related papers  (2026-03-22T03:56:58Z)
• OmniVLN: Omnidirectional 3D Perception and Token-Efficient LLM Reasoning for Visual-Language Navigation across Air and Ground Platforms [33.40889181799252]
  Language-guided embodied navigation requires an agent to interpret object-referential instructions, search across multiple rooms, localize the referenced target, and execute reliable motion toward it.<n>We present OmniVLN, a zero-shot visual-language navigation framework that couples omnidirectional 3D perception with token-efficient hierarchical reasoning for both aerial and ground robots.<n> Experiments show that the proposed hierarchical interface improves spatial referring accuracy from 77.27% to 93.18%, reduces cumulative prompt tokens by up to 61.7% in cluttered multi-room settings, and improves navigation success by up to 11.68% over a flat-
  arXiv  Detail & Related papers  (2026-03-18T04:26:30Z)
• Seeing Where to Deploy: Metric RGB-Based Traversability Analysis for Aerial-to-Ground Hidden Space Inspection [48.120690574682875]
  We present a metric RGB-based geometric-semantic reconstruction and traversability analysis framework for aerial-to-ground hidden space inspection.<n>Experiments on a tethered UAV-UGV platform demonstrate reliable deployment-zone identification in hidden space scenarios.
  arXiv  Detail & Related papers  (2026-03-15T22:20:59Z)
• WildOS: Open-Vocabulary Object Search in the Wild [12.098091049832965]
  This work presents WildOS, a unified system for long-range, open-vocabulary object search.<n>We use a foundation-model-based vision module, ExploRFM, to score frontier nodes of the graph.<n>We also introduce a particle-filter-based method for coarse localization of the open-vocabulary target query.
  arXiv  Detail & Related papers  (2026-02-22T19:14:00Z)
• FOM-Nav: Frontier-Object Maps for Object Goal Navigation [65.76906445210112]
  FOM-Nav is a framework that enhances exploration efficiency through Frontier-Object Maps and vision-language models.<n>To train FOM-Nav, we automatically construct large-scale navigation datasets from real-world scanned environments.<n> FOM-Nav achieves state-of-the-art performance on the MP3D and HM3D benchmarks, particularly in navigation efficiency metric SPL.
  arXiv  Detail & Related papers  (2025-11-30T18:16:09Z)
• PLANA3R: Zero-shot Metric Planar 3D Reconstruction via Feed-Forward Planar Splatting [56.188624157291024]
  We introduce PLANA3R, a pose-free framework for metric Planar 3D Reconstruction from unposed two-view images.<n>Unlike prior feedforward methods that require 3D plane annotations during training, PLANA3R learns planar 3D structures without explicit plane supervision.<n>We validate PLANA3R on multiple indoor-scene datasets with metric supervision and demonstrate strong generalization to out-of-domain indoor environments.
  arXiv  Detail & Related papers  (2025-10-21T15:15:33Z)
• RAVEN: Resilient Aerial Navigation via Open-Set Semantic Memory and Behavior Adaptation [20.730528223747967]
  RAVEN is a 3D memory-based, behavior tree framework for aerial semantic navigation in unstructured outdoor environments.<n>It uses a spatially consistent semantic voxel-ray map as persistent memory, enabling long-horizon planning and avoiding purely reactive behaviors.<n>RAVEN outperforms baselines by 85.25% in simulation and demonstrate its real-world applicability through deployment on an aerial robot in outdoor field tests.
  arXiv  Detail & Related papers  (2025-09-28T01:43:25Z)
• Move to Understand a 3D Scene: Bridging Visual Grounding and Exploration for Efficient and Versatile Embodied Navigation [54.04601077224252]
  Embodied scene understanding requires not only comprehending visual-spatial information but also determining where to explore next in the 3D physical world.<n>underlinetextbf3D vision-language learning enables embodied agents to effectively explore and understand their environment.<n>model's versatility enables navigation using diverse input modalities, including categories, language descriptions, and reference images.
  arXiv  Detail & Related papers  (2025-07-05T14:15:52Z)
• Object Goal Navigation with Recursive Implicit Maps [92.6347010295396]
  We propose an implicit spatial map for object goal navigation. Our method significantly outperforms the state of the art on the challenging MP3D dataset. We deploy our model on a real robot and achieve encouraging object goal navigation results in real scenes.
  arXiv  Detail & Related papers  (2023-08-10T14:21:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.