无人机协同3D建模助力肯尼亚草原野生动物研究
Exploring the potential of collaborative UAV 3D mapping in Kenyan savanna for wildlife research
- 采用V-SLAM与SfM双框架实现无人机协同实时建模
- 对比传统离线方法，提升野外作业效率与精度
- 适合生态监测、动物栖息地评估等野外研究场景
基于无人机的生物多样性保护应用在数据采集方面展现出显著优势。搭载嵌入式数据处理硬件的无人机平台可支持3D生境建模、监控与巡检等保护任务。高质量的实时场景重建与实时无人机定位有助于优化单机或协同任务中的探索与利用平衡。本文探索了两种协同框架——视觉即时定位与地图构建（V-SLAM）和运动恢复结构（SfM）——在3D建模中的潜力，并将其结果与标准离线方法进行比较。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。