从噪声相机运动与语义分割序列中推断远距离物体的三维位置

摘要

基于连续相机测量的三维物体定位对于安全关键监控任务至关重要，例如无人机森林火灾监测。通常，通过密集深度估计或三维场景重建可以解决相机检测到的物体定位问题。然而，在远距离物体或计算资源受限的任务背景下，这两种方案均不可行。本文展示了如何利用粒子滤波器解决单目标和多目标场景下的定位问题。该方法通过三维模拟和基于全球导航卫星系统（GNSS）相机姿态估计的无人机图像分割序列进行了研究。结果表明，在其他方法失效的情况下，粒子滤波器能够基于相机姿态和图像分割完成实际定位任务。粒子滤波器独立于检测方法，使其能够灵活适应新任务。研究还表明，结合现有的图像分割模型，采用所提方法可实现无人机森林火灾监测。

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 dense depth estimation 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 using particle filters for both single and multiple target scenarios. The method was studied using a 3D simulation and a drone-based image segmentation sequence with global navigation satellite system (GNSS)-based camera pose estimates. The results showed that a particle filter can be used to solve practical localisation tasks based on camera poses and image segments in these situations where other solutions fail. The particle filter is independent of the detection method, making it flexible for new tasks. The study also demonstrates that drone-based wildfire monitoring can be conducted using the proposed method paired with a pre-existing image segmentation model.

从噪声相机运动与语义分割序列中推断远距离物体的三维位置

Finding 3D Positions of Distant Objects from Noisy Camera Movement and Semantic Segmentation Sequences

摘要

Support