STEM: Semantic Target Search and Exploration using MAVs in Cluttered Environments

• URL: http://arxiv.org/abs/2606.00762v1
• Date: Sat, 30 May 2026 15:00:58 GMT
• Title: STEM: Semantic Target Search and Exploration using MAVs in Cluttered Environments
• Abstract summary: 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.
• Score: 17.009653719709213
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous target search is crucial for deploying Micro Aerial Vehicles (MAVs) in emergency response and rescue missions. Existing approaches either focus on 2D semantic navigation in structured environments -- which is less effective in complex 3D settings, or on robotic exploration in cluttered spaces -- which often lacks the semantic reasoning needed for efficient target search. This paper overcomes these limitations by proposing a novel framework that utilizes a semantically-guided viewpoint planner to minimize target search and exploration time in unstructured 3D environments using an MAV. Specifically, we develop a combinatorial planner that generates efficient semantic exploration plans by prioritizing viewpoints that likely lead to the target. To guide the planner towards the target, an active perception pipeline is developed that propagates semantic priorities of observed objects into neighboring frontier voxels for computing semantic information gains of frontier viewpoints. In addition, we demonstrate how LLM-based similarity scores can be leveraged as semantic priority input to our pipeline. Evaluations in two distinct simulation environments show that the proposed method consistently outperforms baselines by quickly finding the target while maintaining reasonable exploration times. Real-world experiments with an MAV further demonstrate the method's ability to handle practical constraints like limited battery life, small sensor range, and semantic uncertainty.

Related papers

• Spatial-Semantic Reasoning using Large Language Models for Efficient UAV Search Operations [2.012425476229879]
  We present a real-time semantic navigation framework for Unmanned Aerial Vehicles (UAVs) focused on improving time efficiency in the Object Goal Navigation (ObjectNav) task.<n>A Large Language Model (LLM) interprets user-provided natural language instructions and performs semantic reasoning over detected objects.<n>Experiments in both simulated and real-world settings demonstrate reductions in mission duration while maintaining high search accuracy.
  arXiv  Detail & Related papers  (2026-08-28T12:33:49Z)
• 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)
• 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)
• 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)
• 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)
• Semantic Exploration and Dense Mapping of Complex Environments using Ground Robot with Panoramic LiDAR-Camera Fusion [10.438142938687326]
  This paper presents a system for autonomous semantic exploration and dense semantic target mapping of a complex unknown environment using a ground robot equipped with a LiDAR-panoramic camera suite.<n>We first redefine the task as completing both geometric coverage and semantic viewpoint observation. We then manage semantic and geometric viewpoints separately and propose a novel Priority-driven Decoupled Local Sampler to generate local viewpoint sets.<n>In addition, we propose a Safe Aggressive Exploration State Machine, which allows aggressive exploration behavior while ensuring the robot's safety.
  arXiv  Detail & Related papers  (2025-05-28T21:27:32Z)
• Towards Autonomous UAV Visual Object Search in City Space: Benchmark and Agentic Methodology [26.90429412601702]
  We introduce CityAVOS, the first benchmark dataset for autonomous search of common urban objects.<n>This dataset comprises 2,420 tasks across six object categories with varying difficulty levels.<n>We also propose PRPSearcher, a novel agentic method powered by multi-modal large language models (MLLMs) that mimics human three-tier cognition.
  arXiv  Detail & Related papers  (2025-05-13T17:34:54Z)
• OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments [77.0399450848749]
  We propose an OccNeRF method for training occupancy networks without 3D supervision. We parameterize the reconstructed occupancy fields and reorganize the sampling strategy to align with the cameras' infinite perceptive range. For semantic occupancy prediction, we design several strategies to polish the prompts and filter the outputs of a pretrained open-vocabulary 2D segmentation model.
  arXiv  Detail & Related papers  (2023-12-14T18:58:52Z)
• Long-HOT: A Modular Hierarchical Approach for Long-Horizon Object Transport [83.06265788137443]
  We address key challenges in long-horizon embodied exploration and navigation by proposing a new object transport task and a novel modular framework for temporally extended navigation. Our first contribution is the design of a novel Long-HOT environment focused on deep exploration and long-horizon planning. We propose a modular hierarchical transport policy (HTP) that builds a topological graph of the scene to perform exploration with the help of weighted frontiers.
  arXiv  Detail & Related papers  (2022-10-28T05:30:49Z)
• 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)
• SOON: Scenario Oriented Object Navigation with Graph-based Exploration [102.74649829684617]
  The ability to navigate like a human towards a language-guided target from anywhere in a 3D embodied environment is one of the 'holy grail' goals of intelligent robots. Most visual navigation benchmarks focus on navigating toward a target from a fixed starting point, guided by an elaborate set of instructions that depicts step-by-step. This approach deviates from real-world problems in which human-only describes what the object and its surrounding look like and asks the robot to start navigation from anywhere.
  arXiv  Detail & Related papers  (2021-03-31T15:01:04Z)
• 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.