SGE: Semantically-Guided Exploration for Unstructured Environments via Image-Space Waypoint Sampling
- URL: http://arxiv.org/abs/2608.29315v1
- Date: Sat, 29 Aug 2026 14:58:22 GMT
- Title: SGE: Semantically-Guided Exploration for Unstructured Environments via Image-Space Waypoint Sampling
- Abstract summary: This work introduces Semantically-Guided Exploration (SGE), a modular exploration framework for ground vehicles.<n>SGE integrates pixel-level semantic segmentation into sampling-based waypoint selection and receding-horizon route optimization.
- Score: 3.1921155114863407
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This work introduces Semantically-Guided Exploration (SGE), a modular exploration framework for ground vehicles that integrates pixel-level semantic segmentation into sampling-based waypoint selection and receding-horizon route optimization. Unlike conventional geometric exploration methods, SGE evaluates candidate exploration goals directly in the image space using a semantic-aware utility function that accounts for terrain traversability, obstacle proximity, objects of interest, and depth-based exploration reward. Sampled waypoints are projected into 3D and ordered through a real-time Traveling Salesman Problem (TSP) formulation, enabling receding-horizon goal selection. To address real-world navigation uncertainty, the framework introduces mechanisms, including temporary taboo regions to handle navigation failures and a graph-based relocation strategy for efficient backtracking across explored areas. We evaluate SGE in standardized simulation benchmarks against state-of-the-art exploration planners and demonstrate competitive performance in volumetric coverage, while enabling semantic task biasing that cannot be achieved by purely geometric methods. The framework is further validated through real-world experiments using multiple robotic platforms in indoor campus buildings and in limestone and coal mines. Results show consistent performance and adaptability across platforms and domains.
Related papers
- Hierarchical 3D Scene Graph Construction and Belief-based Planning for Semantic Navigation [34.08516675160383]
We propose a zero-shot semantic navigation framework for embodied agents.<n>Our method incrementally maintains an online Hierarchical 3D Scene Graph (HSG) over objects, zones, and regions, serving as a compact state abstraction for global planning.<n>Building on this memory, we introduce a hierarchical belief-based planning framework that fuses semantic priors with exploration evidence on the HSG, and performs finite-horizon rollouts on an HSG-based simulator to explicitly estimate the long-term expected returns of candidate macro-actions.
arXiv Detail & Related papers (2026-06-30T03:01:44Z) - Semantic-Aware Autonomous Exploration for UAVs in Unknown Indoor Environments [2.379911867541422]
This paper proposes a semantic-aware exploration framework that adds semantic information to a roadmap-based exploration strategy.<n>A semantic reward function is introduced to prioritize regions containing meaningful objects and structures, enabling the UAV to focus on areas with higher information value.<n> Experimental results in multiple simulated environments demonstrate that the proposed approach achieves exploration coverage rates between 90% and 94%.
arXiv Detail & Related papers (2026-06-21T21:07:40Z) - SpaceVLN: A Zero-Shot Vision-and-Language Navigation Agent with Online Spatial Cognitive Memory and Reasoning [59.64305326980364]
SpaceVLN is a navigation agent built around Spatial Cognitive Memory and Task-Guided Spatial Reasoning.<n>Built on this memory, Spatial-CoT integrates task-progress reasoning with spatial perception, analysis, and prediction.<n>Across R2R-CE, RxR-CE, GN-Bench, and HM3D-OVON, SpaceVLN achieves state-of-the-art zero-shot performance.
arXiv Detail & Related papers (2026-06-08T03:42:08Z) - STEM: Semantic Target Search and Exploration using MAVs in Cluttered Environments [17.009653719709213]
This paper proposes a novel framework that utilizes a semantically-guided viewpoint planner to minimize target search and exploration time.<n>Specifically, we develop a planner that generates efficient semantic exploration plans by prioritizing viewpoints that likely lead to the target.
arXiv Detail & Related papers (2026-05-30T15:00:58Z) - GeoMamba: A Geometry-driven MambaVision Framework and Dataset for Fine-grained Optical-SAR Object Retrieval [54.741349848771144]
GeoMamba is a geometry-driven framework for optical-SAR fine-grained retrieval.<n>GFI module enhances cross-modal feature interaction and incorporates structural priors.<n>GeoMamba outperforms existing methods, achieving 63.3% mAP and 77.0% Rank-1 accuracy in all-to-all retrieval setting.
arXiv Detail & Related papers (2026-05-19T12:08:09Z) - SignNav: Leveraging Signage for Semantic Visual Navigation in Large-Scale Indoor Environments [57.79171900005793]
Humans routinely leverage semantic hints provided by signage to navigate to destinations within Large-Scale Indoor environments.<n>This paper introduces a novel embodied navigation task, SignNav, which requires the agent to interpret semantic hint from signage and reason about the subsequent action based on current observation.<n>We propose the spatial-aware module grounds the semantic hint of signage into physical world, while the temporal-aware module captures long-range dependencies between historical states and current observation.
arXiv Detail & Related papers (2026-03-17T06:36:26Z) - 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) - From Reactive to Map-Based AI: Tuned Local LLMs for Semantic Zone Inference in Object-Goal Navigation [0.7734726150561086]
Object-Goal Navigation (ObjectNav) requires an agent to find and navigate to a target object category in unknown environments.<n>We propose a transition from reactive AI to "Map-Based AI" by integrating LLM-based semantic inference with a hybrid topological-grid mapping system.<n>Our framework employs a fine-tuned Llama-2 model via Low-Rank Adaptation (LoRA) to infer semantic zone categories and target existence probabilities from verbalized object observations.
arXiv Detail & Related papers (2026-03-09T08:27:28Z) - Real-time Spatial-temporal Traversability Assessment via Feature-based Sparse Gaussian Process [15.756895046886406]
We propose a novel spatial-temporal traversability assessment method, which aims to enable autonomous robots to navigate through complex terrains.<n>Our method outper- forms SOTA approaches in both accuracy and computational efficiency.<n>We develop an autonomous navigation framework integrated with the traversability map and validate it with a differential driven vehicle in complex outdoor envi- ronments.
arXiv Detail & Related papers (2025-03-06T06:26:57Z) - Occupancy Anticipation for Efficient Exploration and Navigation [97.17517060585875]
We propose occupancy anticipation, where the agent uses its egocentric RGB-D observations to infer the occupancy state beyond the visible regions.
By exploiting context in both the egocentric views and top-down maps our model successfully anticipates a broader map of the environment.
Our approach is the winning entry in the 2020 Habitat PointNav Challenge.
arXiv Detail & Related papers (2020-08-21T03:16:51Z) - Object Goal Navigation using Goal-Oriented Semantic Exploration [98.14078233526476]
This work studies the problem of object goal navigation which involves navigating to an instance of the given object category in unseen environments.
We propose a modular system called, Goal-Oriented Semantic Exploration' which builds an episodic semantic map and uses it to explore the environment efficiently.
arXiv Detail & Related papers (2020-07-01T17:52:32Z)
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.