Exploring the potential of collaborative UAV 3D mapping in Kenyan savanna for wildlife research
- URL: http://arxiv.org/abs/2409.15914v1
- Date: Tue, 24 Sep 2024 09:33:02 GMT
- Title: Exploring the potential of collaborative UAV 3D mapping in Kenyan savanna for wildlife research
- Abstract summary: Biodiversity-based conservation applications have exhibited many data acquisition advantages for researchers.
UAV platforms with embedded data processing hardware can support conservation challenges through 3D habitat mapping, surveillance and monitoring solutions.
- Score: 4.530895788213463
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: UAV-based biodiversity conservation applications have exhibited many data acquisition advantages for researchers. UAV platforms with embedded data processing hardware can support conservation challenges through 3D habitat mapping, surveillance and monitoring solutions. High-quality real-time scene reconstruction as well as real-time UAV localization can optimize the exploration vs exploitation balance of single or collaborative mission. In this work, we explore the potential of two collaborative frameworks - Visual Simultaneous Localization and Mapping (V-SLAM) and Structure-from-Motion (SfM) for 3D mapping purposes and compare results with standard offline approaches.
Related papers
- SpatialUAV: Benchmarking Spatial Intelligence for Low-Altitude UAV Perception, Collaboration, and Motion [76.8430169655641]
spatial intelligence is essential for low-altitude unmanned aerial vehicle (UAV) perception, collaboration, and navigation.<n>We introduce SpatialUAV, a real low-altitude UAV benchmark covering semantic discrimination, spatial relation, aerial-aerial collaboration, aerial-ground collaboration, and motion understanding.<n>We show that current models remain far from human-level performance, with pronounced bottlenecks in cross-view association, structured grounding, geometric reasoning, and temporal viewpoint understanding.
arXiv Detail & Related papers (2026-06-26T09:16:55Z) - Unifying UAV Cross-View Geo-Localization via 3D Geometric Perception [51.687842983240564]
Cross-view geo-localization for Unmanned Aerial Vehicles (UAVs) remains challenging due to the severe geometric discrepancy between oblique UAV imagery and satellite maps.<n>We propose a geometry-aware UAV geo-localization framework that explicitly models the 3D scene geometry to unify coarse place recognition and fine-grained pose estimation.<n>Our method significantly outperforms state-of-the-art baselines, achieving robust meter-level localization accuracy and improved generalization in complex urban environments.
arXiv Detail & Related papers (2026-04-02T08:08:41Z) - Beyond Matching to Tiles: Bridging Unaligned Aerial and Satellite Views for Vision-Only UAV Navigation [51.286599397552756]
We present Bearing-UAV, a vision-driven cross-view navigation method that jointly predicts UAV absolute location and heading from neighboring features.<n>We also present Bearing-UAV-90k, a benchmark for evaluating cross-view localization and navigation.
arXiv Detail & Related papers (2026-03-23T16:17:39Z) - UAV-MM3D: A Large-Scale Synthetic Benchmark for 3D Perception of Unmanned Aerial Vehicles with Multi-Modal Data [47.317955428393134]
We introduce UAV-MM3D, a high-fidelity multimodal synthetic dataset for low-altitude UAV perception and motion understanding.<n>It comprises 400K synchronized frames across diverse scenes (urban areas, suburbs, forests, coastal regions) and weather conditions.<n>Each frame provides 2D/3D bounding boxes, 6-DoF poses, and instance-level annotations, enabling core tasks related to UAVs such as 3D detection, pose estimation, target tracking, and short-term trajectory forecasting.
arXiv Detail & Related papers (2025-11-27T12:30:28Z) - A Tri-Modal Dataset and a Baseline System for Tracking Unmanned Aerial Vehicles [74.8162337823142]
MM-UAV is the first large-scale benchmark for Multi-Modal UAV Tracking.<n>The dataset spans over 30 challenging scenarios, with 1,321 synchronised multi-modal sequences, and more than 2.8 million annotated frames.<n>Accompanying the dataset, we provide a novel multi-modal multi-UAV tracking framework.
arXiv Detail & Related papers (2025-11-23T08:42:17Z) - SpectralCA: Bi-Directional Cross-Attention for Next-Generation UAV Hyperspectral Vision [0.0]
The relevance of this research lies in the growing demand for unmanned aerial vehicles capable of operating reliably in complex environments.<n>Hyperspectral imaging (HSI) provides unique opportunities for UAV-based computer vision.<n>The aim of this work is to develop a deep learning architecture integrating HSI into UAV perception for navigation, object detection, and terrain classification.
arXiv Detail & Related papers (2025-10-10T22:53:28Z) - Move to Understand a 3D Scene: Bridging Visual Grounding and Exploration for Efficient and Versatile Embodied Navigation [54.04601077224252]
Embodied scene understanding requires not only comprehending visual-spatial information but also determining where to explore next in the 3D physical world.<n>underlinetextbf3D vision-language learning enables embodied agents to effectively explore and understand their environment.<n>model's versatility enables navigation using diverse input modalities, including categories, language descriptions, and reference images.
arXiv Detail & Related papers (2025-07-05T14:15:52Z) - VisLanding: Monocular 3D Perception for UAV Safe Landing via Depth-Normal Synergy [0.0]
VisLanding is a perception-based framework for safe UAV (Unmanned Aerial Vehicle) landing.<n>The proposed method exhibits superior generalization and robustness in cross-domain testing compared to other approaches.
arXiv Detail & Related papers (2025-06-17T13:51:16Z) - Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark [15.405137983083875]
Aerial-ground cooperation offers a promising solution by integrating UAVs' aerial views with ground vehicles' local observations.
This paper presents a comprehensive solution for aerial-ground cooperative 3D perception through three key contributions.
arXiv Detail & Related papers (2025-03-10T07:00:07Z) - MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations [55.022519020409405]
This paper builds the first largest ever multi-modal 3D scene dataset and benchmark with hierarchical grounded language annotations, MMScan.
The resulting multi-modal 3D dataset encompasses 1.4M meta-annotated captions on 109k objects and 7.7k regions as well as over 3.04M diverse samples for 3D visual grounding and question-answering benchmarks.
arXiv Detail & Related papers (2024-06-13T17:59:30Z) - UCDNet: Multi-UAV Collaborative 3D Object Detection Network by Reliable Feature Mapping [14.401624713578737]
Multi-UAV collaborative 3D object detection can perceive and comprehend complex environments.
We propose an unparalleled camera-based multi-UAV collaborative 3D object detection paradigm called UCDNet.
We show our method increases 4.7% and 10% mAP respectively compared to the baseline.
arXiv Detail & Related papers (2024-06-07T05:27:32Z) - Multi-View Attentive Contextualization for Multi-View 3D Object Detection [19.874148893464607]
We present Multi-View Attentive Contextualization (MvACon), a simple yet effective method for improving 2D-to-3D feature lifting in query-based 3D (MV3D) object detection.
In experiments, the proposed MvACon is thoroughly tested on the nuScenes benchmark, using both the BEVFormer and its recent 3D deformable attention (DFA3D) variant, as well as the PETR.
arXiv Detail & Related papers (2024-05-20T17:37:10Z) - RadOcc: Learning Cross-Modality Occupancy Knowledge through Rendering
Assisted Distillation [50.35403070279804]
3D occupancy prediction is an emerging task that aims to estimate the occupancy states and semantics of 3D scenes using multi-view images.
We propose RadOcc, a Rendering assisted distillation paradigm for 3D Occupancy prediction.
arXiv Detail & Related papers (2023-12-19T03:39:56Z) - Semi-Perspective Decoupled Heatmaps for 3D Robot Pose Estimation from
Depth Maps [66.24554680709417]
Knowing the exact 3D location of workers and robots in a collaborative environment enables several real applications.
We propose a non-invasive framework based on depth devices and deep neural networks to estimate the 3D pose of robots from an external camera.
arXiv Detail & Related papers (2022-07-06T08:52:12Z) - UAVs Beneath the Surface: Cooperative Autonomy for Subterranean Search
and Rescue in DARPA SubT [5.145696432159643]
This paper presents a novel approach for autonomous cooperating UAVs in search and rescue operations in subterranean domains with complex topology.
The proposed system was ranked second in the Virtual Track of the DARPA SubT Finals as part of the team CTU-CRAS-NORLAB.
The proposed solution also proved to be a robust system for deployment onboard physical UAVs flying in the extremely harsh and confined environment of the real-world competition.
arXiv Detail & Related papers (2022-06-16T13:54:33Z) - A Review on Viewpoints and Path-planning for UAV-based 3D Reconstruction [3.0479044961661708]
3D reconstruction using the data captured by UAVs is also attracting attention in research and industry.
This review paper investigates a wide range of model-free and model-based algorithms for viewpoint and path planning for 3D reconstruction of large-scale objects.
arXiv Detail & Related papers (2022-05-07T20:29:39Z) - Vision-Based UAV Self-Positioning in Low-Altitude Urban Environments [20.69412701553767]
Unmanned Aerial Vehicles (UAVs) rely on satellite systems for stable positioning.
In such situations, vision-based techniques can serve as an alternative, ensuring the self-positioning capability of UAVs.
This paper presents a new dataset, DenseUAV, which is the first publicly available dataset designed for the UAV self-positioning task.
arXiv Detail & Related papers (2022-01-23T07:18:55Z) - Trajectory Design for UAV-Based Internet-of-Things Data Collection: A
Deep Reinforcement Learning Approach [93.67588414950656]
In this paper, we investigate an unmanned aerial vehicle (UAV)-assisted Internet-of-Things (IoT) system in a 3D environment.
We present a TD3-based trajectory design for completion time minimization (TD3-TDCTM) algorithm.
Our simulation results show the superiority of the proposed TD3-TDCTM algorithm over three conventional non-learning based baseline methods.
arXiv Detail & Related papers (2021-07-23T03:33:29Z) - 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) - OmniSLAM: Omnidirectional Localization and Dense Mapping for
Wide-baseline Multi-camera Systems [88.41004332322788]
We present an omnidirectional localization and dense mapping system for a wide-baseline multiview stereo setup with ultra-wide field-of-view (FOV) fisheye cameras.
For more practical and accurate reconstruction, we first introduce improved and light-weighted deep neural networks for the omnidirectional depth estimation.
We integrate our omnidirectional depth estimates into the visual odometry (VO) and add a loop closing module for global consistency.
arXiv Detail & Related papers (2020-03-18T05:52:10Z)
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.