论文标题
主动深度估计：稳定性分析及其应用
Active Depth Estimation: Stability Analysis and its Applications
论文作者
论文摘要
恢复周围环境的3D结构是任何视觉控制结构（SFM）方案的重要任务。本文重点介绍了SFM的理论特性，称为增量活性深度估计。术语增量代表在图像帧的时间顺序上估算场景的3D结构。主动意味着相机驱动可以提高估计性能。从已知的深度估计过滤器开始，本文根据相机的控制输入介绍了过滤器的稳定性分析。通过使用Lyapunov理论分析估计量的收敛性，与先前的结果相比，我们放宽了图像平面中3D点的投影的约束。但是，我们的方法能够处理相机的有限视野约束。主要结果通过模拟数据实验验证。
Recovering the 3D structure of the surrounding environment is an essential task in any vision-controlled Structure-from-Motion (SfM) scheme. This paper focuses on the theoretical properties of the SfM, known as the incremental active depth estimation. The term incremental stands for estimating the 3D structure of the scene over a chronological sequence of image frames. Active means that the camera actuation is such that it improves estimation performance. Starting from a known depth estimation filter, this paper presents the stability analysis of the filter in terms of the control inputs of the camera. By analyzing the convergence of the estimator using the Lyapunov theory, we relax the constraints on the projection of the 3D point in the image plane when compared to previous results. Nonetheless, our method is capable of dealing with the cameras' limited field-of-view constraints. The main results are validated through experiments with simulated data.
