Drone
-
Interpretable Multimodal Gesture Recognition for Drone and Mobile Robot Teleoperation via Log-Likelihood Ratio Fusion
2026-02-27 05:52:04Seungyeol Baek, Jaspreet Singh, Lala Shakti Swarup Ray, Hymalai Bello, Paul Lukowicz, Sungho SuhAbstract
Human operators are still frequently exposed to hazardous environments such as disaster zones and industrial facilities, where intuitive and reliable teleoperation of mobile robots and Unmanned Aerial Vehicles (UAVs) is essential. In this context, hands-free teleoperation enhances operator mobility and situational awareness, thereby improving safety in hazardous environments. While vision-based gesture recognition has been explored as one method for hands-free teleoperation, its performance often deteriorates under occlusions, lighting variations, and cluttered backgrounds, limiting its applicability in real-world operations. To overcome these limitations, we propose a multimodal gesture recognition framework that integrates inertial data (accelerometer, gyroscope, and orientation) from Apple Watches on both wrists with capacitive sensing signals from custom gloves. We design a late fusion strategy based on the log-likelihood ratio (LLR), which not only enhances recognition performance but also provides interpretability by quantifying modality-specific contributions. To support this research, we introduce a new dataset of 20 distinct gestures inspired by aircraft marshalling signals, comprising synchronized RGB video, IMU, and capacitive sensor data. Experimental results demonstrate that our framework achieves performance comparable to a state-of-the-art vision-based baseline while significantly reducing computational cost, model size, and training time, making it well suited for real-time robot control. We therefore underscore the potential of sensor-based multimodal fusion as a robust and interpretable solution for gesture-driven mobile robot and drone teleoperation.
Abstract (translated)
人类操作员仍然经常暴露在危险环境中，如灾区和工业设施中，在这些地方，直观且可靠的移动机器人和无人驾驶飞行器（UAV）远程操控是至关重要的。在这种情况下，无手操控制（hands-free teleoperation）能够增强操作人员的机动性和情境意识，从而提高危险环境中的安全性。虽然基于视觉的手势识别已被探索为一种无手操控的方法，但由于遮挡、光照变化和杂乱背景的影响，其性能经常下降，限制了其实用性。 为了克服这些局限性，我们提出了一种多模态手势识别框架，该框架集成了来自双腕Apple Watch的惯性数据（加速度计、陀螺仪和方向）以及定制手套提供的电容感应信号。我们设计了一种基于对数似然比（LLR）的晚期融合策略，这种策略不仅提升了识别性能，还通过量化各模式的具体贡献提供了可解释性。 为了支持这项研究，我们引入了一个新的数据集，该数据集中包含20个不同的手势，这些手势源自飞机引导信号。这个新数据集包括同步的RGB视频、IMU和电容传感器数据。实验结果表明，我们的框架在性能上与最先进的基于视觉的方法相媲美，同时显著降低了计算成本、模型大小和训练时间，使其非常适合实时机器人控制。 因此，我们强调传感器驱动多模态融合作为手势驱动移动机器人和无人机远程操控的稳健且可解释解决方案的巨大潜力。
URL
https://arxiv.org/abs/2602.23694
PDF
-
Spatially Aware Deep Learning for Microclimate Prediction from High-Resolution Geospatial Imagery
2026-02-27 05:45:10Idan Sulami, Alon Itzkovitch, Michael R. Kearney, Moni Shahar, Ofir LevyAbstract
Microclimate models are essential for linking climate to ecological processes, yet most physically based frameworks estimate temperature independently for each spatial unit and rely on simplified representations of lateral heat exchange. As a result, the spatial scales over which surrounding environmental conditions influence local microclimates remain poorly quantified. Here, we show how remote sensing can help quantify the contribution of spatial context to microclimate temperature predictions. Building on convolutional neural network principles, we designed a task-specific deep neural network and trained a series of models in which the spatial extent of input data was systematically varied. Drone-derived spatial layers and meteorological data were used to predict ground temperature at a focal location, allowing direct assessment of how prediction accuracy changes with increasing spatial context. Our results show that incorporating spatially adjacent information substantially improves prediction accuracy, with diminishing returns beyond spatial extents of approximately 5-7 m. This characteristic scale indicates that ground temperatures are influenced not only by local surface properties, but also by horizontal heat transfer and radiative interactions operating across neighboring microhabitats. The magnitude of spatial effects varied systematically with time of day, microhabitat type, and local environmental characteristics, highlighting context-dependent spatial coupling in microclimate formation. By treating deep learning as a diagnostic tool rather than solely a predictive one, our approach provides a general and transferable method for quantifying spatial dependencies in microclimate models and informing the development of hybrid mechanistic-data-driven approaches that explicitly account for spatial interactions while retaining physical interpretability.
Abstract (translated)
URL
https://arxiv.org/abs/2603.13273
PDF
-
Small Object Detection Model with Spatial Laplacian Pyramid Attention and Multi-Scale Features Enhancement in Aerial Images
2026-02-26 14:11:30Zhangjian Ji, Huijia Yan, Shaotong Qiao, Kai Feng, Wei WeiAbstract
Detecting objects in aerial images confronts some significant challenges, including small size, dense and non-uniform distribution of objects over high-resolution images, which makes detection inefficient. Thus, in this paper, we proposed a small object detection algorithm based on a Spatial Laplacian Pyramid Attention and Multi-Scale Feature Enhancement in aerial images. Firstly, in order to improve the feature representation of ResNet-50 on small objects, we presented a novel Spatial Laplacian Pyramid Attention (SLPA) module, which is integrated after each stage of ResNet-50 to identify and emphasize important local regions. Secondly, to enhance the model's semantic understanding and features representation, we designed a Multi-Scale Feature Enhancement Module (MSFEM), which is incorporated into the lateral connections of C5 layer for building Feature Pyramid Network (FPN). Finally, the features representation quality of traditional feature pyramid network will be affected because the features are not aligned when the upper and lower layers are fused. In order to handle it, we utilized deformable convolutions to align the features in the fusion processing of the upper and lower levels of the Feature Pyramid Network, which can help enhance the model's ability to detect and recognize small objects. The extensive experimental results on two benchmark datasets: VisDrone and DOTA demonstrate that our improved model performs better for small object detection in aerial images compared to the original algorithm.
Abstract (translated)
在空中图像中检测对象面临一些重要的挑战，包括对象尺寸小、密度高且分布不均，这使得检测效率低下。因此，在本文中，我们提出了一种基于空间拉普拉斯金字塔注意（Spatial Laplacian Pyramid Attention）和多尺度特征增强的空中图像中小目标检测算法。 首先，为了提高ResNet-50在小对象上的特征表示能力，我们提出了一种新颖的空间拉普拉斯金字塔注意力模块(SLPA)，该模块被集成到ResNet-50的每个阶段之后，以识别并强调重要的局部区域。其次，为增强模型的语义理解和特征表示能力，我们设计了一个多尺度特征增强模块(MSFEM)，将其整合到C5层的横向连接中，用于构建特征金字塔网络（FPN）。最后，传统特征金字塔网络中的特征表示质量会受到影响，因为在上、下层融合时特征没有对齐。为了解决这一问题，我们在特征金字塔网络的上、下层级融合处理过程中使用了可变形卷积来对齐特征，这有助于增强模型检测和识别小对象的能力。 在VisDrone和DOTA两个基准数据集上的广泛实验结果表明，相较于原始算法，我们的改进型模型在空中图像中小目标检测方面表现更佳。
URL
https://arxiv.org/abs/2602.23031
PDF
-
Constructive Vector Fields for Path Following in Fully-Actuated Systems on Matrix Lie Groups
2026-02-25 00:06:08Felipe Bartelt, Vinicius M. Gon\c{c}alves, Luciano C. A. PimentaAbstract
This paper presents a novel vector field strategy for controlling fully-actuated systems on connected matrix Lie groups, ensuring convergence to and traversal along a curve defined on the group. Our approach generalizes our previous work (Rezende et al., 2022) and reduces to it when considering the Lie group of translations in Euclidean space. Since the proofs in Rezende et al. (2022) rely on key properties such as the orthogonality between the convergent and traversal components, we extend these results by leveraging Lie group properties. These properties also allow the control input to be non-redundant, meaning it matches the dimension of the Lie group, rather than the potentially larger dimension of the space in which the group is embedded. This can lead to more practical control inputs in certain scenarios. A particularly notable application of our strategy is in controlling systems on SE(3) -- in this case, the non-redundant input corresponds to the object's mechanical twist -- making it well-suited for controlling objects that can move and rotate freely, such as omnidirectional drones. In this case, we provide an efficient algorithm to compute the vector field. We experimentally validate the proposed method using a robotic manipulator to demonstrate its effectiveness.
Abstract (translated)
本文提出了一种新的向量场策略，用于在连接矩阵李群上的完全驱动系统进行控制，确保能够收敛到并沿着该群上定义的曲线行进。我们的方法扩展了我们先前的工作（Rezende等人，2022年），并在考虑欧几里得空间中的平移李群时退化为前者。由于Rezende等人的证明（2022）依赖于诸如收敛分量和遍历分量之间正交性之类的特性，我们在本文中通过利用李群的属性来扩展这些结果。这些性质还允许控制输入不冗余，即其维度与李群的维度相匹配，而不仅仅是该群嵌入的空间可能更大的维度。在某些情况下，这可以导致更实用的控制输入。我们策略的一个特别值得注意的应用是在SE(3)上的系统控制——在这种情况下，非冗余输入对应于物体的机械扭动——使其非常适合用于可以自由移动和旋转的对象，例如全方位无人机。在这个情况下，我们提供了一种计算向量场的有效算法，并通过使用机器人操作器进行实验验证来展示该方法的效果。
URL
https://arxiv.org/abs/2602.21450
PDF
-
Event-Aided Sharp Radiance Field Reconstruction for Fast-Flying Drones
2026-02-24 17:02:56Rong Zou, Marco Cannici, Davide ScaramuzzaAbstract
Fast-flying aerial robots promise rapid inspection under limited battery constraints, with direct applications in infrastructure inspection, terrain exploration, and search and rescue. However, high speeds lead to severe motion blur in images and induce significant drift and noise in pose estimates, making dense 3D reconstruction with Neural Radiance Fields (NeRFs) particularly challenging due to their high sensitivity to such degradations. In this work, we present a unified framework that leverages asynchronous event streams alongside motion-blurred frames to reconstruct high-fidelity radiance fields from agile drone flights. By embedding event-image fusion into NeRF optimization and jointly refining event-based visual-inertial odometry priors using both event and frame modalities, our method recovers sharp radiance fields and accurate camera trajectories without ground-truth supervision. We validate our approach on both synthetic data and real-world sequences captured by a fast-flying drone. Despite highly dynamic drone flights, where RGB frames are severely degraded by motion blur and pose priors become unreliable, our method reconstructs high-fidelity radiance fields and preserves fine scene details, delivering a performance gain of over 50% on real-world data compared to state-of-the-art methods.
Abstract (translated)
高速飞行的空中机器人在电池限制条件下能够快速进行检查，具有广泛的应用前景，例如基础设施检测、地形探索和搜索救援。然而，高速度会导致图像中出现严重的运动模糊，并引发姿态估计中的显著漂移和噪声，使得使用神经辐射场（NeRFs）进行密集三维重建变得尤为困难，因为它们对这类退化非常敏感。 在此工作中，我们提出了一种统一框架，利用异步事件流与运动模糊帧结合来从敏捷无人机飞行中重建高保真度的辐射场。通过将事件-图像融合嵌入到NeRF优化过程中，并使用事件和帧两种模态共同细化基于事件的姿态视觉惯性测距先验知识，我们的方法能够在没有地面真实监督的情况下恢复清晰的辐射场以及准确的相机轨迹。 我们在合成数据集及由高速飞行无人机捕获的真实世界序列上验证了该方法的有效性。即使在高度动态的无人机飞行中，RGB帧严重受运动模糊影响且姿态先验变得不可靠时，我们的方法仍能重建高保真的辐射场并保留精细场景细节，在真实世界数据上的表现比现有最佳方法高出超过50%。
URL
https://arxiv.org/abs/2602.21101
PDF
-
EKF-Based Depth Camera and Deep Learning Fusion for UAV-Person Distance Estimation and Following in SAR Operations
2026-02-24 14:37:36Luka \v{S}iktar, Branimir \'Caran, Bojan \v{S}ekoranja, Marko \v{S}vacoAbstract
Search and rescue (SAR) operations require rapid responses to save lives or property. Unmanned Aerial Vehicles (UAVs) equipped with vision-based systems support these missions through prior terrain investigation or real-time assistance during the mission itself. Vision-based UAV frameworks aid human search tasks by detecting and recognizing specific individuals, then tracking and following them while maintaining a safe distance. A key safety requirement for UAV following is the accurate estimation of the distance between camera and target object under real-world conditions, achieved by fusing multiple image modalities. UAVs with deep learning-based vision systems offer a new approach to the planning and execution of SAR operations. As part of the system for automatic people detection and face recognition using deep learning, in this paper we present the fusion of depth camera measurements and monocular camera-to-body distance estimation for robust tracking and following. Deep learning-based filtering of depth camera data and estimation of camera-to-body distance from a monocular camera are achieved with YOLO-pose, enabling real-time fusion of depth information using the Extended Kalman Filter (EKF) algorithm. The proposed subsystem, designed for use in drones, estimates and measures the distance between the depth camera and the human body keypoints, to maintain the safe distance between the drone and the human target. Our system provides an accurate estimated distance, which has been validated against motion capture ground truth data. The system has been tested in real time indoors, where it reduces the average errors, root mean square error (RMSE) and standard deviations of distance estimation up to 15,3\% in three tested scenarios.
Abstract (translated)
搜索和救援（SAR）行动要求快速响应以拯救生命或财产。装备有基于视觉系统的无人飞行器（UAVs）支持通过预先的地形调查或在任务本身中实时协助来执行这些任务。基于视觉的无人机框架通过检测并识别特定个体，然后跟踪和跟随他们，同时保持安全距离，从而帮助人类搜索任务。无人机跟随的关键安全性要求是在真实条件下准确估计相机与目标对象之间的距离，这是通过融合多种图像模态实现的。装备有基于深度学习的视觉系统的无人机为SAR操作的规划和执行提供了一种新方法。 本文中，在自动人员检测和面部识别的系统框架内，我们提出了一种结合深度摄像头测量数据和单目摄像头到人体的距离估算的方法，以实现稳健跟踪和跟随。通过YOLO-pose实现了基于深度学习的深度摄像头数据过滤以及从单目摄像头估计相机与人体之间的距离，从而能够使用扩展卡尔曼滤波器（EKF）算法实时融合深度信息。 我们设计的子系统旨在用于无人机，并且可以估算并测量深度摄像机与人类关键点之间和人体之间的距离，以维持无人机与人类目标之间的安全距离。我们的系统提供了准确的距离估计，在与运动捕捉地面真实数据对比后得到了验证。在室内环境下进行实测时，该系统可以在三种测试场景中将平均误差、均方根误差（RMSE）和距离估算的标准差减少高达15.3%。 这段文字详细描述了一种利用深度学习技术来改进无人机搜索与救援任务的创新方法，并且展示了其在实际应用中的有效性。
URL
https://arxiv.org/abs/2602.20958
PDF
-
ParkDiffusion++: Ego Intention Conditioned Joint Multi-Agent Trajectory Prediction for Automated Parking using Diffusion Models
2026-02-24 14:01:33Jiarong Wei, Anna Rehr, Christian Feist, Abhinav ValadaAbstract
Automated parking is a challenging operational domain for advanced driver assistance systems, requiring robust scene understanding and interaction reasoning. The key challenge is twofold: (i) predict multiple plausible ego intentions according to context and (ii) for each intention, predict the joint responses of surrounding agents, enabling effective what-if decision-making. However, existing methods often fall short, typically treating these interdependent problems in isolation. We propose ParkDiffusion++, which jointly learns a multi-modal ego intention predictor and an ego-conditioned multi-agent joint trajectory predictor for automated parking. Our approach makes several key contributions. First, we introduce an ego intention tokenizer that predicts a small set of discrete endpoint intentions from agent histories and vectorized map polylines. Second, we perform ego-intention-conditioned joint prediction, yielding socially consistent predictions of the surrounding agents for each possible ego intention. Third, we employ a lightweight safety-guided denoiser with different constraints to refine joint scenes during training, thus improving accuracy and safety. Fourth, we propose counterfactual knowledge distillation, where an EMA teacher refined by a frozen safety-guided denoiser provides pseudo-targets that capture how agents react to alternative ego intentions. Extensive evaluations demonstrate that ParkDiffusion++ achieves state-of-the-art performance on the Dragon Lake Parking (DLP) dataset and the Intersections Drone (inD) dataset. Importantly, qualitative what-if visualizations show that other agents react appropriately to different ego intentions.
Abstract (translated)
自动泊车是高级驾驶辅助系统的一个具有挑战性的操作领域，需要强大的场景理解和交互推理能力。主要的挑战在于两个方面：（i）根据上下文预测多个合理的自身意图；（ii）对于每个意图，预测周围代理的联合响应，从而实现有效的假设情景决策制定。然而，现有的方法往往难以满足这些相互依赖的问题需求，在处理这些问题时通常将其孤立对待。 我们提出了ParkDiffusion++，这是一种同时学习多模态自身意图预测器和基于自身条件的多代理联合轨迹预测器的方法，用于自动化泊车系统。我们的方法做出了几个关键贡献： 1. 我们引入了一个自身意图编码器（ego intention tokenizer），它可以从代理历史记录和向量化的地图折线图中预测一组离散的终端意图。 2. 我们进行了基于自身意图条件下的联合预测，为每一种可能的自身意图生成周围代理的社会一致性预测结果。 3. 在训练期间，我们采用了一种轻量级的安全导向去噪器，并根据不同约束对联合场景进行细化处理，从而提高准确性和安全性。 4. 我们提出了反事实知识蒸馏技术，在这种方法中，经过冻结安全导向去噪器优化的EMA教师模型提供伪目标，捕捉代理对替代自身意图的反应方式。 广泛的评估表明，ParkDiffusion++在Dragon Lake Parking (DLP) 数据集和Intersections Drone (inD) 数据集上实现了最先进的性能。值得注意的是，从定性的假设情景可视化结果中可以看出，其他代理能够适当响应不同的自身意图变化。
URL
https://arxiv.org/abs/2602.20923
PDF
-
Visual Cooperative Drone Tracking for Open-Path Gas Measurements
2026-02-24 11:00:23Marius Schaab, Alisha Kiefer, Thomas Wiedemann, Patrick Hinsen, Achim J. LilienthalAbstract
Open-path Tunable Diode Laser Absorption Spectroscopy offers an effective method for measuring, mapping, and monitoring gas concentrations, such as leaking CO2 or methane. Compared to spatial sampling of gas distributions using in-situ sensors, open-path sensors in combination with gas tomography algorithms can cover large outdoor environments faster in a non-invasive way. However, the requirement of a dedicated reflection surface for the open-path laser makes automating the spatial sampling process challenging. This publication presents a robotic system for collecting open-path measurements, making use of a sensor mounted on a ground-based pan-tilt unit and a small drone carrying a reflector. By means of a zoom camera, the ground unit visually tracks red LED markers mounted on the drone and aligns the sensor's laser beam with the reflector. Incorporating GNSS position information provided by the drone's flight controller further improves the tracking approach. Outdoor experiments validated the system's performance, demonstrating successful autonomous tracking and valid CO2 measurements at distances up to 60 meters. Furthermore, the system successfully measured a CO2 plume without interference from the drone's propulsion system, demonstrating its superiority compared to flying in-situ sensors.
Abstract (translated)
开放式路径可调二极管激光吸收光谱法提供了一种有效的方法，用于测量、绘制和监测气体浓度，例如泄漏的二氧化碳或甲烷。与使用原位传感器的空间采样相比，结合气体层析算法的开放式路径传感器能够以非侵入性方式更快地覆盖大型户外环境。然而，开放路径激光器需要专用的反射面，这使得空间采样的自动化过程具有挑战性。 本文介绍了一种用于收集开放路径测量数据的机器人系统，该系统利用安装在地面水平旋转装置上的传感器和携带反射镜的小型无人机组成。通过配备变焦相机，地面单元可以视觉追踪固定在无人机上的红色LED标记，并将传感器的激光束对准反射镜。进一步结合由无人机飞行控制器提供的GNSS位置信息，提高了跟踪方法的效果。 户外实验验证了该系统的性能，在长达60米的距离内实现了成功的自主跟踪和有效的二氧化碳测量结果。此外，该系统还成功地在没有受到无人机推进系统干扰的情况下测定了一个二氧化碳羽流的存在，这证明了其相对于飞行原位传感器的优势。
URL
https://arxiv.org/abs/2602.20768
PDF
-
Large-scale Photorealistic Outdoor 3D Scene Reconstruction from UAV Imagery Using Gaussian Splatting Techniques
2026-02-23 20:40:26Christos Maikos, Georgios Angelidis, Georgios Th. PapadopoulosAbstract
In this study, we present an end-to-end pipeline capable of converting drone-captured video streams into high-fidelity 3D reconstructions with minimal latency. Unmanned aerial vehicles (UAVs) are extensively used in aerial real-time perception applications. Moreover, recent advances in 3D Gaussian Splatting (3DGS) have demonstrated significant potential for real-time neural rendering. However, their integration into end-to-end UAV-based reconstruction and visualization systems remains underexplored. Our goal is to propose an efficient architecture that combines live video acquisition via RTMP streaming, synchronized sensor fusion, camera pose estimation, and 3DGS optimization, achieving continuous model updates and low-latency deployment within interactive visualization environments that supports immersive augmented and virtual reality (AR/VR) applications. Experimental results demonstrate that the proposed method achieves competitive visual fidelity, while delivering significantly higher rendering performance and substantially reduced end-to-end latency, compared to NeRF-based approaches. Reconstruction quality remains within 4-7\% of high-fidelity offline references, confirming the suitability of the proposed system for real-time, scalable augmented perception from aerial platforms.
Abstract (translated)
在这项研究中，我们提出了一种端到端的流水线，能够将无人机捕捉的视频流转换为高质量的3D重建模型，并且延迟极低。无人驾驶飞行器（UAV）在实时空中感知应用中被广泛使用。此外，最近关于3D高斯点阵（3D Gaussian Splatting, 3DGS）的进步已经展示了其实时神经渲染的巨大潜力。然而，它们集成到基于无人机的端到端重建和可视化系统中的方法仍需进一步探索。 我们的目标是提出一种高效的架构，该架构结合了通过RTMP流进行实时视频采集、传感器同步融合、摄像机姿态估计以及3DGS优化，从而实现持续模型更新，并在支持沉浸式增强现实（AR）和虚拟现实（VR）应用的交互可视化环境中部署低延迟。实验结果表明，所提出的方法实现了竞争性的视觉保真度，同时提供了比NeRF方法显著更高的渲染性能和大幅减少的端到端延迟。 重建质量与高质量离线参考之间的差距维持在4-7%，这证明了该系统适用于从空中平台进行实时、可扩展的增强感知。
URL
https://arxiv.org/abs/2602.20342
PDF
-
Training Deep Stereo Matching Networks on Tree Branch Imagery: A Benchmark Study for Real-Time UAV Forestry Applications
2026-02-23 12:12:43Yida Lin, Bing Xue, Mengjie Zhang, Sam Schofield, Richard GreenAbstract
Autonomous drone-based tree pruning needs accurate, real-time depth estimation from stereo cameras. Depth is computed from disparity maps using $Z = f B/d$, so even small disparity errors cause noticeable depth mistakes at working distances. Building on our earlier work that identified DEFOM-Stereo as the best reference disparity generator for vegetation scenes, we present the first study to train and test ten deep stereo matching networks on real tree branch images. We use the Canterbury Tree Branches dataset -- 5,313 stereo pairs from a ZED Mini camera at 1080P and 720P -- with DEFOM-generated disparity maps as training targets. The ten methods cover step-by-step refinement, 3D convolution, edge-aware attention, and lightweight designs. Using perceptual metrics (SSIM, LPIPS, ViTScore) and structural metrics (SIFT/ORB feature matching), we find that BANet-3D produces the best overall quality (SSIM = 0.883, LPIPS = 0.157), while RAFT-Stereo scores highest on scene-level understanding (ViTScore = 0.799). Testing on an NVIDIA Jetson Orin Super (16 GB, independently powered) mounted on our drone shows that AnyNet reaches 6.99 FPS at 1080P -- the only near-real-time option -- while BANet-2D gives the best quality-speed balance at 1.21 FPS. We also compare 720P and 1080P processing times to guide resolution choices for forestry drone systems.
Abstract (translated)
自主无人机树修剪需要来自立体相机的准确实时深度估计。深度是通过使用公式 $Z = f B/d$（其中 $d$ 是视差）从视差图计算出来的，因此即使是微小的视差误差也会导致在作业距离下明显的深度错误。基于我们之前的研究，该研究确定了DEFOM-Stereo是在植被场景中生成最佳参考视差图的方法，我们现在首次对十种深度立体匹配网络进行了训练和测试，并使用真实的树干图像数据集进行评估。使用的数据集是坎特伯雷树干数据集——包含5,313对从ZED Mini相机拍摄的1080P和720P分辨率下的立体图像，以及由DEFOM生成的视差图作为训练目标。 这十种方法涵盖了逐步细化、三维卷积、边缘感知注意力机制及轻量级设计。通过使用感知指标（SSIM、LPIPS、ViTScore）和结构化指标（SIFT/ORB特征匹配），我们发现BANet-3D在整体质量上表现最佳（SSIM = 0.883，LPIPS = 0.157），而RAFT-Stereo则在场景理解方面得分最高（ViTScore = 0.799）。在我们的无人机上安装的独立供电NVIDIA Jetson Orin Super (16 GB) 上进行测试后发现，在1080P分辨率下，AnyNet达到了每秒6.99帧——这是唯一接近实时选择，而BANet-2D则以1.21 FPS的速度提供了最佳的质量速度平衡。我们还比较了720P和1080P的处理时间，以此来指导林业无人机系统的分辨率选择。 该研究为开发用于自主树修剪任务的高效深度感知系统提供了有价值的见解，并指出了在不同应用场景下的最优方案。
URL
https://arxiv.org/abs/2602.19763
PDF
-
A Text-Guided Vision Model for Enhanced Recognition of Small Instances
2026-02-23 04:40:14Hyun-Ki JungAbstract
As drone-based object detection technology continues to evolve, the demand is shifting from merely detecting objects to enabling users to accurately identify specific targets. For example, users can input particular targets as prompts to precisely detect desired objects. To address this need, an efficient text-guided object detection model has been developed to enhance the detection of small objects. Specifically, an improved version of the existing YOLO-World model is introduced. The proposed method replaces the C2f layer in the YOLOv8 backbone with a C3k2 layer, enabling more precise representation of local features, particularly for small objects or those with clearly defined boundaries. Additionally, the proposed architecture improves processing speed and efficiency through parallel processing optimization, while also contributing to a more lightweight model design. Comparative experiments on the VisDrone dataset show that the proposed model outperforms the original YOLO-World model, with precision increasing from 40.6% to 41.6%, recall from 30.8% to 31%, F1 score from 35% to 35.5%, and mAP@0.5 from 30.4% to 30.7%, confirming its enhanced accuracy. Furthermore, the model demonstrates superior lightweight performance, with the parameter count reduced from 4 million to 3.8 million and FLOPs decreasing from 15.7 billion to 15.2 billion. These results indicate that the proposed approach provides a practical and effective solution for precise object detection in drone-based applications.
Abstract (translated)
随着基于无人机的目标检测技术的不断进步，用户的需求已从单纯的物体识别转向了能够精准定位特定目标的方向。例如，用户可以通过输入具体的提示词来精确地探测到想要找的对象。为了满足这一需求，开发了一种高效的文本引导式目标检测模型，以提高对小目标的检测效果。具体来说，这是现有YOLO-World模型的一个改进版本。 所提出的方法在YOLOv8骨干网络中用C3k2层替换了原有的C2f层，这使得局部特征的表示更加精确，特别是在处理较小物体或边界清晰的目标时表现尤为突出。此外，该架构通过优化并行处理提高了处理速度和效率，并且有助于构建更为轻量化的模型设计。 在VisDrone数据集上的比较实验表明，所提出的模型优于原始的YOLO-World模型，在精度（从40.6%提升至41.6%）、召回率（从30.8%增加到31%）、F1分数（从35%提高到35.5%）以及mAP@0.5指标上均有改善，这证实了其在精确度上的显著提升。此外，该模型还展示了出色的轻量化性能，在参数数量上减少了20万至380万个，并将计算量（FLOPs）从157亿次减少到了152亿次。 这些结果显示所提出的方法为基于无人机应用的精准目标检测提供了一个实用且有效的解决方案。
URL
https://arxiv.org/abs/2602.19503
PDF
-
SIGMAS: Second-Order Interaction-based Grouping for Overlapping Multi-Agent Swarms
2026-02-23 01:43:56Minah Lee, Saibal MukhopadhyayAbstract
Swarming systems, such as drone fleets and robotic teams, exhibit complex dynamics driven by both individual behaviors and emergent group-level interactions. Unlike traditional multi-agent domains such as pedestrian crowds or traffic systems, swarms typically consist of a few large groups with inherent and persistent memberships, making group identification essential for understanding fine-grained behavior. We introduce the novel task of group prediction in overlapping multi-agent swarms, where latent group structures must be inferred directly from agent trajectories without ground-truth supervision. To address this challenge, we propose SIGMAS (Second-order Interaction-based Grouping for Multi-Agent Swarms), a self-supervised framework that goes beyond direct pairwise interactions and model second-order interaction across agents. By capturing how similarly agents interact with others, SIGMAS enables robust group inference and adaptively balances individual and collective dynamics through a learnable gating mechanism for joint reasoning. Experiments across diverse synthetic swarm scenarios demonstrate that SIGMAS accurately recovers latent group structures and remains robust under simultaneously overlapping swarm dynamics, establishing both a new benchmark task and a principled modeling framework for swarm understanding.
Abstract (translated)
集群系统，例如无人机群和机器人团队，展现出由个体行为以及群体层面的涌现性交互共同驱动的复杂动态。与传统的多智能体领域（如行人人群或交通系统）不同，集群通常包含少数几个具有固有且持久成员身份的大组，因此识别这些小组对于理解精细粒度的行为至关重要。我们引入了重叠多智能体群集中的群体预测这一新任务，在该任务中需要直接从智能体轨迹推断出潜在的群体结构，而无需地面真实监督。 为了应对这个挑战，我们提出了SIGMAS（基于二阶互动的多智能体集群分组），这是一个自监督框架，它超越了直接成对交互，同时建模智能体之间的二阶互作。通过捕捉代理如何与他人相互作用的方式，SIGMAS能够实现稳健的群体推断，并通过一个可学习的门控机制进行联合推理来适应性地平衡个体和集体动态。 跨多种合成集群场景的实验表明，SIGMAS可以准确恢复潜在的群组结构，并在同时重叠的群集动力学下保持鲁棒性。这不仅建立了新的基准任务，还为群集理解提供了一个原则性的建模框架。
URL
https://arxiv.org/abs/2603.00120
PDF
-
Bumper Drone: Elastic Morphology Design for Aerial Physical Interaction
2026-02-21 22:44:40Pongporn Supa, Alex Dunnett, Feng Xiao, Rui Wu, Mirko Kovac, Basaran Bahadir KocerAbstract
Aerial robots are evolving from avoiding obstacles to exploiting the environmental contact interactions for navigation, exploration and manipulation. A key challenge in such aerial physical interactions lies in handling uncertain contact forces on unknown targets, which typically demand accurate sensing and active control. We present a drone platform with elastic horns that enables touch-and-go manoeuvres - a self-regulated, consecutive bumping motion that allows the drone to maintain proximity to a wall without relying on active obstacle avoidance. It leverages environmental interaction as a form of embodied control, where low-level stabilisation and near-obstacle navigation emerge from the passive dynamic responses of the drone-obstacle system that resembles a mass-spring-damper system. Experiments show that the elastic horn can absorb impact energy while maintaining vehicle stability, reducing pitch oscillations by 38% compared to the rigid horn configuration. The lower horn arrangement was found to reduce pitch oscillations by approximately 54%. In addition to intermittent contact, the platform equipped with elastic horns also demonstrates stable, sustained contact with static objects, relying on a standard attitude PID controller.
Abstract (translated)
空中机器人正从避障进化为利用环境接触交互来进行导航、探索和操作。此类物理互动中的一个关键挑战在于处理在未知目标上不确定的接触力，这通常需要准确的感应和主动控制。我们介绍了一种装有弹性触角的无人机平台，它能够执行“触碰与前进”的机动动作——这是一种自我调节、连续碰撞运动方式，允许无人机不依赖于主动避障就能保持靠近墙壁的位置。该平台利用环境互动作为嵌入式控制的一种形式，在这种情况下，低级稳定和接近障碍物导航源自类似质量-弹簧-阻尼系统的无人机-障碍物系统被动动态响应。 实验表明，弹性触角可以在吸收冲击能量的同时维持车辆稳定性，相比刚性触角配置，可将俯仰摆动减少38%。较低位置安装的触角被发现可以将俯仰摆动大约减少54%。除了间歇接触之外，装有弹性触角的平台还展示了与静态物体稳定、持续接触的能力，并且依靠标准的姿态PID控制器来实现这一点。
URL
https://arxiv.org/abs/2602.18976
PDF
-
Bluetooth Phased-array Aided Inertial Navigation Using Factor Graphs: Experimental Verification
2026-02-19 14:34:04Glen Hjelmerud M{\o}rkbak S{\o}rensen, Torleiv H. Bryne, Kristoffer Gryte, Tor Arne JohansenAbstract
Phased-array Bluetooth systems have emerged as a low-cost alternative for performing aided inertial navigation in GNSS-denied use cases such as warehouse logistics, drone landings, and autonomous docking. Basing a navigation system off of commercial-off-the-shelf components may reduce the barrier of entry for phased-array radio navigation systems, albeit at the cost of significantly noisier measurements and relatively short feasible range. In this paper, we compare robust estimation strategies for a factor graph optimisation-based estimator using experimental data collected from multirotor drone flight. We evaluate performance in loss-of-GNSS scenarios when aided by Bluetooth angular measurements, as well as range or barometric pressure.
Abstract (translated)
URL
https://arxiv.org/abs/2602.17407
PDF
-
A High-Level Survey of Optical Remote Sensing
2026-02-19 14:26:26Panagiotis Koletsis, Vasilis Efthymiou, Maria Vakalopoulou, Nikos Komodakis, Anastasios Doulamis, Georgios Th. PapadopoulosAbstract
In recent years, significant advances in computer vision have also propelled progress in remote sensing. Concurrently, the use of drones has expanded, with many organizations incorporating them into their operations. Most drones are equipped by default with RGB cameras, which are both robust and among the easiest sensors to use and interpret. The body of literature on optical remote sensing is vast, encompassing diverse tasks, capabilities, and methodologies. Each task or methodology could warrant a dedicated survey. This work provides a comprehensive overview of the capabilities of the field, while also presenting key information, such as datasets and insights. It aims to serve as a guide for researchers entering the field, offering high-level insights and helping them focus on areas most relevant to their interests. To the best of our knowledge, no existing survey addresses this holistic perspective.
Abstract (translated)
URL
https://arxiv.org/abs/2602.17397
PDF
-
One Agent to Guide Them All: Empowering MLLMs for Vision-and-Language Navigation via Explicit World Representation
2026-02-17 07:13:48Zerui Li, Hongpei Zheng, Fangguo Zhao, Aidan Chan, Jian Zhou, Sihao Lin, Shijie Li, Qi WuAbstract
A navigable agent needs to understand both high-level semantic instructions and precise spatial perceptions. Building navigation agents centered on Multimodal Large Language Models (MLLMs) demonstrates a promising solution due to their powerful generalization ability. However, the current tightly coupled design dramatically limits system performance. In this work, we propose a decoupled design that separates low-level spatial state estimation from high-level semantic planning. Unlike previous methods that rely on predefined, oversimplified textual maps, we introduce an interactive metric world representation that maintains rich and consistent information, allowing MLLMs to interact with and reason on it for decision-making. Furthermore, counterfactual reasoning is introduced to further elicit MLLMs' capacity, while the metric world representation ensures the physical validity of the produced actions. We conduct comprehensive experiments in both simulated and real-world environments. Our method establishes a new zero-shot state-of-the-art, achieving 48.8\% Success Rate (SR) in R2R-CE and 42.2\% in RxR-CE benchmarks. Furthermore, to validate the versatility of our metric representation, we demonstrate zero-shot sim-to-real transfer across diverse embodiments, including a wheeled TurtleBot 4 and a custom-built aerial drone. These real-world deployments verify that our decoupled framework serves as a robust, domain-invariant interface for embodied Vision-and-Language navigation.
Abstract (translated)
URL
https://arxiv.org/abs/2602.15400
PDF
-
A Retrieval-Augmented Language Assistant for Unmanned Aircraft Safety Assessment and Regulatory Compliance
2026-02-16 08:13:53Gabriele Immordino, Andrea Vaiuso, Marcello RighiAbstract
This paper presents the design and validation of a retrieval-based assistant that supports safety assessment, certification activities, and regulatory compliance for unmanned aircraft systems. The work is motivated by the growing complexity of drone operations and the increasing effort required by applicants and aviation authorities to apply established assessment frameworks, including the Specific Operations Risk Assessment and the Pre-defined Risk Assessment, in a consistent and efficient manner. The proposed approach uses a controlled text-based architecture that relies exclusively on authoritative regulatory sources. To enable traceable and auditable outputs, the assistant grounds each response in retrieved passages and enforces citation-driven generation. System-level controls address common failure modes of generative models, including fabricated statements, unsupported inferences, and unclear provenance, by separating evidence storage from language generation and by adopting conservative behavior when supporting documentation is insufficient. The assistant is intentionally limited to decision support; it does not replace expert judgment and it does not make autonomous determinations. Instead, it accelerates context-specific information retrieval and synthesis to improve document preparation and review while preserving human responsibility for critical conclusions. The architecture is implemented using established open-source components, and key choices in retrieval strategy, interaction constraints, and response policies are evaluated for suitability in safety-sensitive regulatory environments. The paper provides technical and operational guidance for integrating retrieval-based assistants into aviation oversight workflows while maintaining accountability, traceability, and regulatory compliance.
Abstract (translated)
本文介绍了设计和验证一种基于检索的辅助系统，该系统支持无人驾驶航空系统的安全性评估、认证活动及法规遵从性。这项工作是受到无人机操作日益复杂化以及申请人和航空监管机构在一致且高效地应用现有评估框架（如具体操作风险评估和预定义风险评估）方面所需努力不断增加这一情况所驱动的。 提出的解决方案采用了一种基于受控文本架构，该架构仅依赖于权威法规来源。为了确保输出可追溯性和可审计性，辅助系统将每个响应都依据检索到的段落来生成，并强制执行引用驱动的产生机制。在系统层面采取控制措施以应对生成模型常见的故障模式，包括伪造陈述、缺乏支持的推理以及模糊出处等问题，通过将证据存储与语言生成相分离并采用保守行为（当支持文件不足时）来解决这些问题。 该辅助工具被有意限于决策支持，它不会取代专家判断也不作出自主决定。相反，它加快了特定情境下的信息检索和合成过程，以提高文档准备和审查效率，同时保留人类对关键结论的责任。架构使用已建立的开源组件实现，并评估在检索策略、交互限制以及响应政策方面的重要选择是否适合安全敏感的监管环境。 本文提供了将基于检索的辅助系统整合到航空监督工作流程中的技术与运营指导，以维持问责制、可追溯性和法规遵从性。
URL
https://arxiv.org/abs/2603.09999
PDF
-
Learning on the Fly: Replay-Based Continual Object Perception for Indoor Drones
2026-02-13 20:34:01Sebastian-Ion Nae, Mihai-Eugen Barbu, Sebastian Mocanu, Marius LeordeanuAbstract
Autonomous agents such as indoor drones must learn new object classes in real-time while limiting catastrophic forgetting, motivating Class-Incremental Learning (CIL). However, most unmanned aerial vehicle (UAV) datasets focus on outdoor scenes and offer limited temporally coherent indoor videos. We introduce an indoor dataset of $14,400$ frames capturing inter-drone and ground vehicle footage, annotated via a semi-automatic workflow with a $98.6\%$ first-pass labeling agreement before final manual verification. Using this dataset, we benchmark 3 replay-based CIL strategies: Experience Replay (ER), Maximally Interfered Retrieval (MIR), and Forgetting-Aware Replay (FAR), using YOLOv11-nano as a resource-efficient detector for deployment-constrained UAV platforms. Under tight memory budgets ($5-10\%$ replay), FAR performs better than the rest, achieving an average accuracy (ACC, $mAP_{50-95}$ across increments) of $82.96\%$ with $5\%$ replay. Gradient-weighted class activation mapping (Grad-CAM) analysis shows attention shifts across classes in mixed scenes, which is associated with reduced localization quality for drones. The experiments further demonstrate that replay-based continual learning can be effectively applied to edge aerial systems. Overall, this work contributes an indoor UAV video dataset with preserved temporal coherence and an evaluation of replay-based CIL under limited replay budgets. Project page: this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2602.13440
PDF
-
LAF-YOLOv10 with Partial Convolution Backbone, Attention-Guided Feature Pyramid, Auxiliary P2 Head, and Wise-IoU Loss for Small Object Detection in Drone Aerial Imagery
2026-02-13 18:23:54Sohail Ali Farooqui, Zuhair Ahmed Khan Taha, Mohammed Mudassir Uddin, Shahnawaz AlamAbstract
Unmanned aerial vehicles serve as primary sensing platforms for surveillance, traffic monitoring, and disaster response, making aerial object detection a central problem in applied computer vision. Current detectors struggle with UAV-specific challenges: targets spanning only a few pixels, cluttered backgrounds, heavy occlusion, and strict onboard computational budgets. This study introduces LAF-YOLOv10, built on YOLOv10n, integrating four complementary techniques to improve small-object detection in drone imagery. A Partial Convolution C2f (PC-C2f) module restricts spatial convolution to one quarter of backbone channels, reducing redundant computation while preserving discriminative capacity. An Attention-Guided Feature Pyramid Network (AG-FPN) inserts Squeeze-and-Excitation channel gates before multi-scale fusion and replaces nearest-neighbor upsampling with DySample for content-aware interpolation. An auxiliary P2 detection head at 160$\times$160 resolution extends localization to objects below 8$\times$8 pixels, while the P5 head is removed to redistribute parameters. Wise-IoU v3 replaces CIoU for bounding box regression, attenuating gradients from noisy annotations in crowded aerial scenes. The four modules address non-overlapping bottlenecks: PC-C2f compresses backbone computation, AG-FPN refines cross-scale fusion, the P2 head recovers spatial resolution, and Wise-IoU stabilizes regression under label noise. No individual component is novel; the contribution is the joint integration within a single YOLOv10 framework. Across three training runs (seeds 42, 123, 256), LAF-YOLOv10 achieves 35.1$\pm$0.3\% mAP@0.5 on VisDrone-DET2019 with 2.3\,M parameters, exceeding YOLOv10n by 3.3 points. Cross-dataset evaluation on UAVDT yields 35.8$\pm$0.4\% mAP@0.5. Benchmarks on NVIDIA Jetson Orin Nano confirm 24.3 FPS at FP16, demonstrating viability for embedded UAV deployment.
Abstract (translated)
URL
https://arxiv.org/abs/2602.13378
PDF
-
Resource-Aware Deployment Optimization for Collaborative Intrusion Detection in Layered Networks
2026-02-12 11:42:58Andr\'e Garc\'ia G\'omez, Ines Rieger, Wolfgang Hotwagner, Max Landauer, Markus Wurzenberger, Florian Skopik, Edgar WeipplAbstract
Collaborative Intrusion Detection Systems (CIDS) are increasingly adopted to counter cyberattacks, as their collaborative nature enables them to adapt to diverse scenarios across heterogeneous environments. As distributed critical infrastructure operates in rapidly evolving environments, such as drones in both civil and military domains, there is a growing need for CIDS architectures that can flexibly accommodate these dynamic changes. In this study, we propose a novel CIDS framework designed for easy deployment across diverse distributed environments. The framework dynamically optimizes detector allocation per node based on available resources and data types, enabling rapid adaptation to new operational scenarios with minimal computational overhead. We first conducted a comprehensive literature review to identify key characteristics of existing CIDS architectures. Based on these insights and real-world use cases, we developed our CIDS framework, which we evaluated using several distributed datasets that feature different attack chains and network topologies. Notably, we introduce a public dataset based on a realistic cyberattack targeting a ground drone aimed at sabotaging critical infrastructure. Experimental results demonstrate that the proposed CIDS framework can achieve adaptive, efficient intrusion detection in distributed settings, automatically reconfiguring detectors to maintain an optimal configuration, without requiring heavy computation, since all experiments were conducted on edge devices.
Abstract (translated)
协作入侵检测系统（CIDS）被越来越多地采用，以应对网络攻击，因为它们的协作性质使它们能够适应异构环境中各种场景的变化。鉴于分布式关键基础设施在快速变化的环境中运行，例如民用和军事领域的无人机，对于可以灵活适应这些动态变化的CIDS架构的需求正在不断增加。在这项研究中，我们提出了一种新型的CIDS框架，旨在易于部署于多样化的分布式环境。该框架根据可用资源和数据类型动态优化每个节点上的检测器分配，从而能够快速适应新的操作场景，并且计算开销极小。 首先，我们进行了全面的文献回顾以确定现有CIDS架构的关键特征。基于这些见解和实际应用场景，我们开发了我们的CIDS框架，并使用包含不同攻击链和网络拓扑的几个分布式数据集对其进行了评估。值得注意的是，我们引入了一个基于针对地面上破坏关键基础设施的无人机进行的真实网络攻击的公开数据集。 实验结果表明，所提出的CIDS框架能够在分布式环境中实现自适应、高效的入侵检测，自动重新配置检测器以保持最优配置，并且无需进行大量计算，因为所有试验均在边缘设备上完成。
URL
https://arxiv.org/abs/2602.11851
PDF
