Drone
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USU-Corn-WeedDB: A UAV RGB Image Dataset for Multi-Species Weed Detection in Forage Corn
2026-06-04 20:51:41Utsav Bhandari, Saroj Burlakoti, Rhonda Miller, Sierra Young, Eric Westra, Aaron EtienneAbstract
Weed pressure in forage corn production causes yield losses of up to 31.5%, yet site-specific weed management (SSWM) systems built on UAV imagery and deep learning remain constrained by the scarcity of field-representative training datasets. We present USU-Corn-WeedDB, a publicly available UAV RGB image dataset collected from a commercial forage corn field in Cache Valley, Utah, designed to support multi-class weed detection under both supervised and semi-supervised learning frameworks. RGB imagery was acquired on 27 June 2025 using an Autel EVO II Dual 640T V2 drone at ~10m above ground level, yielding a ground sampling distance of approximately 0.48 cm/pixel. A total of 366 full-resolution images were tiled into 8,800 patches at 640 x 640-pixel resolution. Of these, 800 images were manually annotated for three weed species; common lambsquarters (Chenopodium album), redroot pigweed (Amaranthus retroflexus), and green foxtail (Setaria viridis) comprising 10,539 bounding-box instances, with the remaining 8,000 tiles retained as an unlabeled pool for semi-supervised experiments. This dataset reflects a natural class imbalance where redroot pigweed constitutes 53.86% of annotated instances, which was preserved intentionally to mirror real field conditions. To validate dataset utility, we trained 28 object detection models spanning five architecture families including YOLOv8, YOLOv9, YOLOv10, YOLO11, YOLO26, and RT-DETR under identical conditions without hyperparameter tuning. Test set mAP@0.5 ranged from 0.773 to 0.840, with lightweight models achieving competitive performance relevant to edge-deployed UAV systems. USU-Corn-WeedDB is publicly available at this https URL.
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URL
https://arxiv.org/abs/2606.06709
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Merging model-based control with multi-agent reinforcement learning for multi-agent cooperative teaming strategies
2026-06-04 11:01:00Christian Llanes, Spencer W. Jensen, Samuel CooganAbstract
In this work, we propose a framework that combines multi-agent reinforcement learning (MARL) with model-based control to achieve safe, dynamically feasible actions in cooperative multi-agent tasks. Multi-agent reinforcement learning provides the advantage of learning cooperative policies for multi-agent teams from discrete non-differentiable rewards in a long planning horizon. Model-predictive control is robust and offers safe, dynamically feasible actions in a fast replanning framework for short horizons. We propose an algorithm that extends actor-critic model predictive control for MARL which we refer to as multi-agent actor-critic model predictive control (MA-AC-MPC). We demonstrate the capabilities of this algorithm by applying it to a multi-agent pursuit-evasion scenario. Specifically, we compare the evader team's strategy using the MA-AC-MPC model and a multi-layer perceptron model (MA-AC-MLP). The pursuer team uses augmented proportional navigation as it is accepted as an advanced adversarial control law. We also provide an example with a heterogeneous environment where a drone and omni-wheeled rover cooperate to achieve repeatable and successful landing with 100% success rate in hardware for MA-AC-MPC compared to 60% for MA-AC-MLP. We demonstrate the robustness of the proposed MA-AC-MPC algorithm in hardware for both environments.
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URL
https://arxiv.org/abs/2606.06011
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HDST-GNN: Heterogeneous Dynamic Spatiotemporal Graph Neural Networks for Multi-Object Tracking in UAV Aerial Imagery
2026-06-04 02:04:52Phillip JiangAbstract
Multi-object tracking (MOT) from UAV imagery presents unique challenges: altitude varies across sequences, objects are small and densely packed, and frequent occlusion causes identity switches. Existing graph-based trackers assume fixed spatial context and treat all objects uniformly, ignoring the heterogeneous lifecycle states of detections, active tracklets, and lost targets. We propose HDST-GNN, a Heterogeneous Dynamic Spatiotemporal Graph Neural Network with three novel contributions. First, Altitude-Adaptive Edge Construction estimates a camera-altitude proxy from mean object area and adjusts the graph connectivity radius accordingly. Second, Heterogeneous Node Representation models detections (Type-D), confirmed tracklets (Type-T), and lost tracklets (Type-L) as distinct node types with dedicated projections and typed edge relations. Third, Occlusion-Gated Temporal Aggregation gates each node's attention contribution by its occlusion confidence, preventing occluded nodes from corrupting neighbour embeddings. HDST-GNN is trained end-to-end with a differentiable Sinkhorn head using joint cross-entropy and triplet loss. On VisDrone2019-MOT with oracle detections, HDST-GNN achieves 94.51% MOTA and 97.24% IDF1, outperforming SORT by +5.0 MOTA points and reducing identity switches by 81%. With real YOLOv8n detections, HDST-GNN reduces identity switches by 49% vs. SORT. Ablation studies confirm the independent contribution of each component.
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URL
https://arxiv.org/abs/2606.05587
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AirDreamer: Generalist Drone Navigation with World Models
2026-06-02 07:15:13Zian Liu, Andong Yang, Chunkai Yang, Ruidong An, Chao Gao, Guyue ZhouAbstract
Navigating a drone in unseen and cluttered environments requires reliable generalization to unseen scene layouts and understanding of environmental structure relative to the robot's capabilities. Previous methods, which assume the same environment configuration, often rely heavily on human-designed perception pipelines and predefined rules to guide the robot toward the target. This process is environment-dependent and generalizes poorly across environments. Inspired by animal navigation behavior, we design a navigation framework that navigates with a reinforcement-learning-based policy on top of a world-model-based environment understanding to overcome these issues. In addition, a sparse reward function without hand-crafted shaping terms is designed to avoid local minima traps and encourage yaw control behaviors. In simulation and on real drones, our method exhibits emergent capabilities for navigating complex, unseen environments and escaping local optima where other methods fail. In challenging maps, it achieves a 5.3% higher navigation success rate than best baseline. Furthermore, the proposed framework achieves effective sim-to-real transfer without any tuning during deployment. The code will be publicly available.
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URL
https://arxiv.org/abs/2606.03252
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Market-Based Replanning for Safety-Critical UAV Swarms in Search and Rescue Missions
2026-06-01 09:33:15Luiz Giacomossi, Andrea Haglund, Claire Namatovu, Emily Zainali, Esaias M{\aa}lqvist, Yonatan M. Beyene, Ivan Tomasic, Baran \c{C}\"ur\"ukl\"u, H{\aa}kan ForsbergAbstract
Reliable autonomous UAV swarms in Search and Rescue (SAR) missions require fault-tolerant coordination capable of sustaining operations despite agent degradation. This paper introduces the Intelligent Replanning Drone Swarm (IRDS), a distributed coordination architecture designed for resource-constrained environments. The proposed framework employs a Reverse-Auction market mechanism where agents bid to service search sectors based on a distance-weighted cost function, coupled with a geometric consensus protocol for target verification. We evaluate the approach through physics-based simulations (N=8 agents, 8x8 grid) subjected to stochastic fault injection. Results indicate that the swarm autonomously reallocates tasks from failed agents with low latency relative to the total mission duration, maintaining a mission success rate of 93% under 25% workforce degradation. The proposed framework demonstrates a robust, empirically tested method for self-healing aerial robotic coordination.
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URL
https://arxiv.org/abs/2606.01970
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ForestMamba: Sparse Mamba with Geometry-guided Queries for 3D Forest Point Cloud Segmentation
2026-06-01 01:49:09Trung Thanh Nguyen, Tuan-Anh Vu, Duc Viet Le, Yasutomo Kawanishi, Takahiro Komamizu, Ichiro Ide, Teja KattenbornAbstract
AI-based semantic and instance segmentation of terrestrial and drone LiDAR point clouds is emerging as a transformative approach for converting the complex 3D structure of forests into actionable information for forest monitoring and biodiversity assessment. However, forest LiDAR scenes remain highly challenging due to their large data volumes, irregular sampling density, overlapping and complex canopy structure, and geographic variability. Existing methods based on sparse convolutions or Transformers achieve promising results, but suffer from two key limitations: Quadratic complexity of attention scales poorly to large forest scenes, and Generic context modeling does not exploit forest structural priors, limiting tree separation in complex regions. To address these challenges, we propose ForestMamba, a structure-aware method that incorporates forest-specific priors into feature encoding, query generation, and query refinement, while replacing quadratic attention with linear-time state-space modeling. First, we introduce a sparse encoder with vertical-priority slab serialization that organizes sparse voxels into vertically coherent sequences for efficient long-range context modeling. Second, we propose a geometry-guided query initialization strategy based on an on-the-fly multi-scale Canopy Height Model (CHM), where canopy maxima provide ecologically meaningful query seeds, supplemented by Farthest Point Sampling (FPS) to cover understory trees. Third, we design a Mamba-based query decoder that combines local kNN voxel aggregation with a spatial dual-path Mamba for query refinement with linear computational complexity. Extensive experiments across seven forest regions demonstrate that ForestMamba consistently outperforms existing baselines in both segmentation tasks, while achieving 3 times faster inference and 2.3 times lower GPU memory than Transformer-based methods.
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URL
https://arxiv.org/abs/2606.01549
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Crazyflow: An Accurate, GPU-Accelerated, Differentiable Drone Simulator in JAX
2026-05-31 22:38:46Martin Schuck, Marcel P. Rath, Yufei Hua, AbhisheK Goudar, SiQi Zhou, Angela P. SchoelligAbstract
High-quality, large-scale synthetic data from simulations is becoming a cornerstone for pushing the capabilities of robot algorithms. While aerial robotics simulators have evolved to support specialized needs such as fidelity, differentiability, and swarms independently, a unified platform that can synthesize data across all these domains is missing. In this work, we propose Crazyflow, a simulator designed to push the limits of aerial-robotics algorithm development, from model-based to data-driven methods, gradient-based to sampling-based approaches, and single-agent to multi-agent systems. Compared to existing state-of-the-art drone simulators, it achieves speeds more than an order of magnitude faster for a single drone and can simulate thousands of swarms of 4000 drones each. Real-world experiments show Crazyflow supports both analytical-gradient-based policy learning, achieving sub-centimeter trajectory tracking accuracy without domain randomization, and sampling-based obstacle avoidance at speeds exceeding half a billion steps per second. Breaking the traditional train-then-deploy paradigm, we show that its unprecedented speed even enables in-flight reinforcement learning; we demonstrate this by throwing a physical drone into the air and training a recovery policy from scratch in 0.38 seconds, successfully stabilizing the drone. Crazyflow supports multiple levels of simulation abstraction, is directly compatible with all open-source Crazyflie models, and enables rapid reconfiguration across custom drone platforms and applications by providing a light-weight system identification pipeline. By pushing accuracy, speed, and differentiability simultaneously, Crazyflow serves as an open-source resource for synthetic data generation, with emerging capabilities for large-scale parallelization for online, in-execution learning and optimization, opening the door to novel algorithm development.
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URL
https://arxiv.org/abs/2606.01478
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Reusing Fusion-Time Spectral Reliability for Adaptive Fusion and Expert Routing in RGB-Infrared Object Detection
2026-05-31 11:28:41Yefeng WuAbstract
RGB-infrared detectors typically discard the statistics generated during cross-modal fusion, leaving downstream modules unaware of whether the current interaction is reliable. We propose to extract a parameter-free, 7-dimensional spectral reliability descriptor -- summarizing band energy, amplitude ratio, phase consistency, and cross-modal correlation -- and to reuse it beyond the fusion stage. The descriptor drives both Spectral Reliability Fusion (SRF), which gates a spectral residual against a conservative spatial base, and Reliability-Conditioned Expert Routing (RCER), which combines the descriptor with pooled content to steer sparse post-fusion experts. Under matched ablations, descriptor-aware gating improves mAP50 over content-only adaptive gating; a $2{\times}2$ factorial analysis further shows that descriptor-conditioned routing provides the larger marginal gain over expert architecture alone at near-equal parameter count. Under six synthetic degradations on DroneVehicle, average retention rises to 95.0%, versus 92.0% for content-only MoE and 87.9% for concatenation, with the largest gain under modality drop; the same model also improves mAP50 by +5.2/+5.3 on the natural day/night split. These results suggest that preserving fusion-time reliability as an explicit signal benefits both adaptive fusion and post-fusion conditional computation.
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URL
https://arxiv.org/abs/2606.01173
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FAIR^2 Drones: An AI-Ready Standard for Cross-Domain Wildlife Drone Datasets
2026-05-29 20:58:53Jenna Kline, Kilian Meier, Vandita Shukla, Edouard G. A. Rolland, Elena Iannino, Lucie Laporte-Devylder, Constanza Andrea Molina Catricheo, Blair Costelloe, Elizabeth Campolongo, Henrik S. Midtiby, Devis Tuia, Benjamin Risse, Ulrik P. S. Lundquist, Anders Lyhne Christensen, Fabio Remondino, Thomas Richardson, Tanya Berger-WolfAbstract
Animal ecology data collection using drones represents a substantial investment of time, expertise, and financial resources. Yet most existing datasets serve only a single research community, limiting interdisciplinary reuse. We propose a unified drone dataset standard, FAIR^2 Drones, that bridges ecology, robotics, and computer vision by building on existing FAIR and AI-ready data frameworks while adding essential platform metadata and annotation specifications. Our standard enables datasets to simultaneously support ecological analysis, robotics algorithm development, and computer vision benchmarking. We provide open-source validation tools, reference implementations, and multimodal extensions linking drone imagery with complementary sensors such as camera traps, GPS, and acoustics. By standardizing metadata across disciplines, this framework maximizes the scientific return on investment for costly field deployments and accelerates cross-domain collaboration in environmental monitoring.
Abstract (translated)
URL
https://arxiv.org/abs/2606.00355
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FLIP: Real-Time and Resilient Formation Planning for Large-Scale DIstributed Swarms via Point Cloud Registration
2026-05-28 10:02:50Yuan Zhou, Guangtong Xu, Zhenyu Hou, Jialiang Hou, Fei GaoAbstract
Traditional large-scale formation planning either oversimplify the formation representation which leads to poor performance, or they employ complete collaborative relationships, which results in excessive computational load. To achieve high-performance and large-scale formation planning, we transform the Optimal Formation Position Sequence \cite{c1} (OFPS) calculation problem into a spatiotemporal Point Cloud Registration (PCR) problem. Each agent derives its OFPS by distributively computing the matching result between current positions and the desired formation positions of all other agents. Then each agent optimizes the cooperative formation trajectory by using OFPS. We leverage the PCR method with outlier rejection to rapidly perform large-scale formation position registration. This prevents suboptimal trajectories and failed agents from propagating through the cooperative network and affecting more agents. Consequently, we uniformly achieve resilient, efficient, and distributed trajectory planning for large-scale swarms. The effectiveness and the superiority of the proposed method are demonstrated through large-scale simulations of 120-drone formation, and rigorous benchmarking against state-of-the-art (SOTA) methods.
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URL
https://arxiv.org/abs/2605.29704
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A Progress-Aware Leader-Follower Midair Docking System for Dual-Drone Aerial Manipulation
2026-05-28 06:00:52Yifan Cai, Jan Ming Kevin Tan, Xiangqi Li, Chenzhe Jin, Narsimlu Kemsaram, Valerio ModugnoAbstract
Reliable midair docking between small unmanned aerial vehicles (UAVs) is essential for modular aerial cooperation and manipulation, but it requires precise relative-pose control and repeatable platform under tight thrust and payload constraints. We present a dual-drone docking platform where two quadrotors operate in a leader-follower formation and dock using a lightweight modular frame with passive magnetic latching. A progress-aware mission supervisor manages phase transitions: approach, alignment, capture, and settle. This platform integrates a complete hardware-software stack (ROS 2 with Crazyflie/PX4 interfaces) and synchronized logging for benchmark evaluation. We evaluate the platform in simulation and real-world experiments using quantitative metrics such as formation error, baseline and yaw consistency, docking success rate, time-to-dock, and failure-mode statistics. The platform enables statistically grounded comparison of docking supervision and synchronization strategies and provides a practical testbed for modular aerial cooperation and repeatable midair aerial manipulation.
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URL
https://arxiv.org/abs/2605.29410
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CA-AC-MPC: CUDA-Accelerated Actor-Critic Model Predictive Control
2026-05-27 22:38:09Antoonio Buo, Vittorio Cammarota, Michele Avagnale, Pierluigi Arpenti, Vincenzo Lippiello, Fabio RuggieroAbstract
In the literature, actor-critic model predictive control (AC-MPC) integrates MPC with reinforcement learning to enable high-performance control of complex dynamical systems. However, its differentiable MPC layer requires repeatedly solving an optimization problem in both the forward and backward passes, leading to substantial training and inference latency. This paper tackles this bottleneck introducing a CUDA-accelerated variant that significantly reduces end-to-end execution time while preserving the control performance of the baseline formulation. Simulation results on an agile drone racing task show that our approach achieves state-of-the-art lap times and near-limit dynamic behaviour with markedly reduced training and inference time.
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URL
https://arxiv.org/abs/2605.29155
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Ultra-Reduced-Impact-Encased-Logging : propose a new method for selective sustainable logging and post-harvest silvicultural treatment in tropical forest using airborne robotics systems
2026-05-26 20:30:42Daniel Albiero, Gelton Fernando de Morais, Daniela Han, Fl\'avio Roberto de Freitas Gon\c{c}alves, Artur Vit\'orio Andrade Santos, Wesllen Lins de Ara\'ujo, Alessandra Maia Freire, Cl\'audio Kiyoshi Umezu, Mateus Peressin, Francesco Toscano, Admilson \'Irio Ribeiro, Alfeu J. Sguarezi Filho, Am\'erico Ferraz Dias Neto, Angel Pontin GarciaAbstract
Tropical forests worldwide are under intense deforestation pressure driven by economic and political interests, and scientific evidence suggests this deforestation contributes to climate change. This paper proposes a novel logging method for tropical forests, Ultra-Reduced-Impact-Encased-Logging (URIEL). This new method is based on heli-logging techniques combined with intensive use of robotics and AI integrated with post-harvest silvicultural treatments performed by drones. The concept of appropriate equipment for this method was developed, dimensions were determined, details were completed in a digital proof of concept, and an effective digital simulation and economic feasibility analysis were carried out for various helicopter-timber-distance combinations. The results demonstrated that a URIEL method has high economic viability and makes it possible to virtually eliminate collateral damage to forests while maintaining ecosystem services. The main conclusion of this paper is that, despite the satisfactory scientific and technological results, the feasibility of a Uriel method depends on the integration of stakeholders intrinsic to the context: high-tech industry; political governments; certified logging companies; and native populations.
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URL
https://arxiv.org/abs/2605.28883
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Towards Drone-based Mapping of Volcanic Gases using Gas Tomography
2026-05-26 15:33:56Marius Schaab, Niklas Karbach, Antonia Rabe, Thomas Wiedemann, Patrick Hinsen, Dmitriy Shutin, Thorsten Hoffmann, Achim J. LilienthalAbstract
Volcanoes emit large amounts of CO2, directly influencing human lives. Mapping volcanic gas emissions helps to forecast eruptions and understand the impact of volcanoes on climate and the environment. Drone-based gas sensing significantly reduces risks in volcanic monitoring but faces technical limitations when measuring gas, as rotor downwash disperses the gas plume before detection. Gas Tomography using remote gas sensing addresses this challenge. At the Salinelle dei Cappuccini mud volcanoes, we demonstrate that while drone-mounted in-situ sensors failed to detect CO2 emissions due to aerodynamic disturbance, open-path sensing successfully enabled remote gas distribution mapping. We present a novel model-based gas tomographic reconstruction approach that incorporates a Lagrangian model to compensate for wind-induced advection. The resulting gas distribution maps align with manually collected in-situ measurements, confirming that model-based gas tomography effectively overcomes downwash limitations and enables accurate mapping of volcanic emissions.
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URL
https://arxiv.org/abs/2605.27180
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PEACE: A Planner-Executor Agent with Constraint Enforcement for UAVs
2026-05-26 10:03:22Erdem Uysal, Timo Kehrer, Sebastiano PanichellaAbstract
Foundation models are increasingly used to drive autonomous systems, yet existing approaches either keep the model in a tight control loop, raising latency and hallucination risk, or compile natural language into opaque end-to-end policies that are hard to explain, constraint and require domain-specific datasets and fine-tuning. We propose a planner-executor agent for PX4-based drones that decouples high-level mission planning from low-level control. A large language model performs single-pass task planning, while execution is handled through a structured ROS 2 tool-calling interface bridged to MAVLink. The system constructs a world model by combining modular 2D detectors (e.g., YOLO or vision-language models) with a pinhole depth projection module for 3D object localization. A constraint enforcement layer enforces altitude limits and horizontal geofencing, and bounded replanning enables recovery from execution-time action failures. We position our approach within three common design patterns for foundation-model-based robotics systems and demonstrate its feasibility in PX4 software-in-the-loop simulations in Gazebo. Results highlight improved explainability, constraint enforcement, and reduced LLM calls compared to tightly coupled LLM control. The code, dataset, videos, and other material can be found at the following link: this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2606.00104
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Collaborative Navigation and Exploration with $beta$-Sparse Gaussian Processes
2026-05-25 19:55:59Evangelos Psomiadis, Dipankar Maity, Panagiotis TsiotrasAbstract
Collaborative navigation of heterogeneous robots in unknown environments poses significant challenges due to sensing, communication, and computational limitations. In this work, a lead robot navigates toward a target while a mobile sensor robot (e.g., a drone) assists by transmitting information about its locally observed environment under bandwidth constraints. We propose a framework that enables the sensor to jointly select its transmitted map points and navigation actions online, while also predicting unexplored regions of the environment. To this end, we present $\beta$-Sparse Gaussian Processes, a novel and robust variational sparse Gaussian Process model for task-aware inducing point selection. Furthermore, we develop an action-selection strategy that balances task relevance with exploration. Simulations on Mars and Earth maps show that the framework can reduce path cost by 18% relative to no communication and decrease transmitted information by 76% compared to raw-data transmission baselines.
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URL
https://arxiv.org/abs/2605.26304
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LRDDv3: High-Resolution Long-Range Drone Detection Dataset with Range Information and Thermal Data
2026-05-25 15:20:36Knut Peterson, Zaid Mayers, Azmain Yousuf, Priontu Chowdhury, Asher Zaczepinski, Solmaz Arezoomandan, Reihaneh Maarefdoust, David HanAbstract
Unmanned Aerial Vehicles (UAVs) have quickly become common in various airspaces, representing a wide range of applications from recreation flying to commercial photography and package delivery. With the increasing prevalence of UAVs, it becomes critical that both manned and unmanned aircraft can detect UAVs and other flying objects from long range to effectively track movement and ensure safe operation in shared spaces. While several datasets have been introduced for drone detection, the need for expanded high-quality data persists, especially in the area of high-resolution long-range drone data. To address this, we introduce a high-resolution dataset of 102,532 long-range RGB images of drones, sampled at 5 FPS from 128 distinct video clips taken mid flight during 17 different data collection days spread over 8 months to ensure a wide variety of lighting scenarios, flight locations, and background elements. The dataset boasts comprehensive drone range information across the dataset, as well as 29,630 IR images, all paired with RGB counterparts from the base dataset. As one of the first drone detection datasets to leverage 4K image resolution and paired 640x512 IR images, our work represents a significant advancement to enable the detection of drones at long range. For access to the complete dataset, please visit this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2605.25942
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HoLoArm: Deformable Arms for Collision-Tolerant Quadrotor Flight
2026-05-25 12:37:18Quang Ngoc Pham, Jonas Eschmann, Yang Zhou, Alejandro Ojeda Olarte, Giuseppe Loianno, Van Anh HoAbstract
The increasing use of drones in human-centric applications highlights the need for designs that can survive collisions and recover rapidly, minimizing risks to both humans and the environment. We present HoLoArm, a quadrotor with compliant arms inspired by the nodus structure of dragonfly wings. This design provides natural flexibility and resilience while preserving flight stability, which is further reinforced by the integration of a Reinforcement Learning (RL) control policy that enhances both recovery and hovering performance. Experimental results demonstrate that HoLoArm can passively deform in any direction, including axial one, and recover within 0.3-0.6 s depending on the direction and level of the impact. The drone can survive collisions at speeds up to 7.6 m/s and carry a 540 g payload while maintaining stable flight. This work contributes to the morphological design of soft aerial robots with high agility and reliable safety, enabling operation in cluttered and human shared environments, and lays the groundwork for future fully soft drones that integrate compliant structures with intelligent control.
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URL
https://arxiv.org/abs/2605.25790
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Towards Open-World Referring Expression Comprehension: A Benchmark with Training-free Multi-task Consistency Checker
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Referring expression comprehension (REC) aims to localize a target object within an image based on a given expression. Although recent advances in vision-language models have led to substantial improvements in REC tasks, current REC benchmarks often hold simple scenarios and the assumption that each expression maps to a unique object. These limitations hinder the deployment of REC models in open-world environments. To fill this gap, we introduce OpenRef, a new benchmark for REC in complex visual and linguistic scenarios. OpenRef features three key advancements: 1) Diverse visual scenarios: spanning diverse visual domains, including ground views, drone views, dark scenes and adverse weather conditions; 2) Variable target counts: breaking the single-target limitation with multi-target and none-target samples; 3) Rich vocabulary types: incorporating proper nouns, polysemous words and ordinal terms to fit a wider range of expression needs. Furthermore, as traditional metrics are insufficient for open-world setting, we leverage F1 to measure grounding accuracy and propose N3R (Negative Relative Rejection Reliability) to assess relative rejection reliability against negative expressions. Finally, we introduce Multi-task Consistency Checker (MCC), a training-free but plug-and-play strategy that enhances model performance with one click by enforcing consistency self-verification. Extensive experiments demonstrate that this work significantly advances the performance of existing REC models in complex scenarios, paving the way for open-world REC. Project page: this https URL
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URL
https://arxiv.org/abs/2605.25706
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OPAL: Omnidirectional Path-efficient Aerial 3D expLoration
2026-05-25 04:51:56Yoga Satwik Chappidi, Avideh ZakhorAbstract
Autonomous exploration is critical for robot mapping unknown environments. Desirable characteristics of exploration algorithms include compute efficiency and small traversed distance during the exploration process. Motivated by these, we present Omnidirectional Path-efficient Aerial 3D expLoration (OPAL), an exploration framework centered on deliberate 360-degree yaw rotation at ambiguous branch points rather than compute-heavy global tour planning. We devise multiple variants of OPAL to determine the frontier-selection strategy once the yaw pan is completed. One variant is model-free, while others use large language models (LLMs) or vision-language models (VLMs). We characterize the performance of these variants while varying the vicinity search radius to include frontiers in the selection process. Through simulations we find that although the time-consuming in-place yaw rotation increases total exploration time relative to more computationally complex baselines such as EDEN and FALCON, OPAL is computationally simpler and achieves shorter travel distances and higher coverage-versus-distance area under the curve. We also show that adjusting the frontier-selection search radius enables a tradeoff between travel distance and total exploration time. We verify our results on a Modal AI drone in two indoor environments by comparing OPAL against FALCON, and find that the traveled distance for a variant of OPAL to be as much as 25% lower than FALCON.
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URL
https://arxiv.org/abs/2605.25423
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