Semantic-Aware Autonomous Exploration for UAVs in Unknown Indoor Environments

• URL: http://arxiv.org/abs/2606.22670v1
• Date: Sun, 21 Jun 2026 21:07:40 GMT
• Title: Semantic-Aware Autonomous Exploration for UAVs in Unknown Indoor Environments
• Abstract summary: 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%.
• Score: 2.379911867541422
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous exploration in unknown environments requires unmanned aerial vehicles (UAVs) to efficiently generate informative trajectories while simultaneously constructing accurate maps. Although many existing exploration methods rely on geometric information, they often lack semantic awareness, resulting in suboptimal exploration efficiency and limited environmental understanding. To address this limitation, this paper proposes a semantic-aware exploration framework that adds semantic information to a roadmap-based exploration strategy. The proposed method builds on the Dynamic Exploration Planner (DEP), which incrementally constructs a Probabilistic Roadmap (PRM), and augments this roadmap with a semantic layer. 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. Furthermore, the roadmap is continuously updated to support efficient frontier selection and path planning during exploration. The proposed framework is implemented in ROS Noetic and Gazebo using an RGB-D sensor for simultaneous acquisition of geometric and semantic information. Experimental results in multiple simulated environments demonstrate that the proposed approach achieves exploration coverage rates between 90% and 94% while reducing exploration time and travel distance compared with conventional geometry-based exploration methods.

Related papers

• VANDERER: Map-Free Exploration using Future-Aware and Visual-Curiosity-Guided Diffusion Policy [12.450778241309365]
  VANDERER is an exploration framework that leverages a Visual Curiosity Module to guide pre-trained diffusion policies.<n> evaluated across diverse simulated environments, VANDERER consistently outperforms established baselines.
  arXiv  Detail & Related papers  (2026-06-12T18:33:12Z)
• 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)
• LMPath: Language-Mediated Priors and Path Generation for Aerial Exploration [48.457368728987994]
  We present LMPath, a pipeline for generating language-mediated exploration priors for Unmanned Aerial Vehicle (UAV) search missions.<n>Given a basic geofence and an object of interest prompt, LMPath uses generative language models to determine what regions of the environment should contain that object.<n>To demonstrate it's capabilities, we used LMPath to generate various UAV paths and ran them using a real UAV over large-scale environments.
  arXiv  Detail & Related papers  (2026-05-13T17:02:51Z)
• FrontierNet: Learning Visual Cues to Explore [54.8265603996238]
  This work aims at leveraging 2D visual cues for efficient autonomous exploration, addressing the limitations of extracting goal poses from a 3D map.<n>We propose a visual-only frontier-based exploration system, with FrontierNet as its core component.<n>Our approach provides an alternative to existing 3D-dependent goal-extraction approaches, achieving a 15% improvement in early-stage exploration efficiency.
  arXiv  Detail & Related papers  (2025-01-08T16:25:32Z)
• OTO Planner: An Efficient Only Travelling Once Exploration Planner for Complex and Unknown Environments [6.128246045267511]
  "Only Travelling Once Planner" is an efficient exploration planner that reduces repeated paths in complex environments. It includes fast frontier updating, viewpoint evaluation and viewpoint refinement. It reduces the exploration time and movement distance by 10%-20% and improves the speed of frontier detection by 6-9 times.
  arXiv  Detail & Related papers  (2024-06-11T14:23:48Z)
• Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning [60.599223456298915]
  We propose a novel way to integrate deep learning into exploration by leveraging 3D scene completion for informed, safe, and interpretable mapping and planning. We show that our method can speed up coverage of an environment by 73% compared to the baselines with only minimal reduction in map accuracy. Even if scene completions are not included in the final map, we show that they can be used to guide the robot to choose more informative paths, speeding up the measurement of the scene with the robot's sensors by 35%.
  arXiv  Detail & Related papers  (2022-08-17T14:19:33Z)
• A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments [68.31522961125589]
  We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles. The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map. Results indicate that the proposed multi-UAV system has improvements in terms of time-cost, the proportion of search area surveyed, as well as successful rates for search and rescue missions.
  arXiv  Detail & Related papers  (2021-07-19T12:54:04Z)
• Deep Reinforcement Learning for Adaptive Exploration of Unknown Environments [6.90777229452271]
  We develop an adaptive exploration approach to trade off between exploration and exploitation in one single step for UAVs. The proposed approach uses a map segmentation technique to decompose the environment map into smaller, tractable maps. The results demonstrate that our proposed approach is capable of navigating through randomly generated environments and covering more AoI in less time steps compared to the baselines.
  arXiv  Detail & Related papers  (2021-05-04T16:29:44Z)
• Autonomous UAV Exploration of Dynamic Environments via Incremental Sampling and Probabilistic Roadmap [0.3867363075280543]
  We propose a novel dynamic exploration planner (DEP) for exploring unknown environments using incremental sampling and Probabilistic Roadmap (PRM) Our method safely explores dynamic environments and outperforms the benchmark planners in terms of exploration time, path length, and computational time.
  arXiv  Detail & Related papers  (2020-10-14T22:52:37Z)
• 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)
• Radar-based Dynamic Occupancy Grid Mapping and Object Detection [55.74894405714851]
  In recent years, the classical occupancy grid map approach has been extended to dynamic occupancy grid maps. This paper presents the further development of a previous approach. The data of multiple radar sensors are fused, and a grid-based object tracking and mapping method is applied.
  arXiv  Detail & Related papers  (2020-08-09T09:26:30Z)

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.