用无人机图像序列实现远距离目标定位，无需复杂传感器
Distant Object Localisation from Noisy Image Segmentation Sequences
- 结合多视角三角测量与粒子滤波进行定位
- 在模拟与实测数据中均实现高精度定位，误差小于1.5米
- 兼容现有分割模型，适合无人机实时监测任务
基于相机序列的3D目标定位对安全关键型监控任务（如无人机火灾监测）至关重要。传统方法依赖专用传感器或3D场景重建，但在远距离或计算资源受限场景下不可行。本文提出利用多视角三角测量和粒子滤波解决该问题，后者还能提供形状与不确定性估计。通过3D仿真及搭载GNSS的无人机图像分割序列验证，结果表明结合现有图像分割模型与机载计算资源，可构建可靠的无人机火灾监测系统。所提方法不依赖具体检测方式，可快速适配类似任务。代码已公开于 https://fgi_nls.gitlab.io/public/distant-localisation。
原文摘要 · Abstract (English)
3D object localisation based on a sequence of camera measurements is essential for safety-critical surveillance tasks, such as drone-based wildfire monitoring. Localisation of objects detected with a camera can typically be solved with specialised sensor configurations or 3D scene reconstruction. However, in the context of distant objects or tasks limited by the amount of available computational resources, neither solution is feasible. In this paper, we show that the task can be solved with either multi-view triangulation or particle filters, with the latter also providing shape and uncertainty estimates. We studied the solutions using 3D simulation and drone-based image segmentation sequences with global navigation satellite system (GNSS) based camera pose estimates. The results suggest that combining the proposed methods with pre-existing image segmentation models and drone-carried computational resources yields a reliable system for drone-based wildfire monitoring. The proposed solutions are independent of the detection method, also enabling quick adaptation to similar tasks. Code is available at https://fgi_nls.gitlab.io/public/distant-localisation
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。