图像Quantization噪声下机器人立体摄像机的控制
Controlling a Robotic Stereo Camera Under Image Quantization Noise
摘要：
在本文中，我们解决的问题，控制下的图像量化噪声的移动立体摄像机。假设一组目标的一对图像可用，相机移动通过下一最佳视图（NBV）的序列，即，最小化目标累积状态协方差的轨迹的视图序列，使用捕获图像量化噪声的立体装置的真实模型和将观测历史与新信息融合的卡尔曼滤波器（KF）构造。该算法将控制分解为两个阶段：首先在摄像机相对坐标系中计算NBV，然后在固定的全局坐标系中移动摄像机实现该视图。这种分解允许相机驱动到一个新的姿态，该姿态有效地实现了相机坐标中的NBV，同时满足全局坐标中的视场约束，这是一项使用复杂感测模型特别具有挑战性的任务。我们提供模拟和真实的实验，说明所提出的移动相机系统的能力，以准确地定位目标集。我们还提出了一种新的数据驱动的技术来表征未建模的不确定性，如校准误差，在像素级，并表明这种方法可以确保KF的稳定性。
英文摘要：
In this paper, we address the problem of controlling a mobile stereo camera under image quantization noise. Assuming that a pair of images of a set of targets is available, the camera moves through a sequence of Next-Best-Views (NBVs), i.e., a sequence of views that minimize the trace of the targets' cumulative state covariance, constructed using a realistic model of the stereo rig that captures image quantization noise and a Kalman Filter (KF) that fuses the observation history with new information. The proposed algorithm decomposes control into two stages: first the NBV is computed in the camera relative coordinates, and then the camera moves to realize this view in the fixed global coordinate frame. This decomposition allows the camera to drive to a new pose that effectively realizes the NBV in camera coordinates while satisfying Field-of-View constraints in global coordinates, a task that is particularly challenging using complex sensing models. We provide simulations and real experiments that illustrate the ability of the proposed mobile camera system to accurately localize sets of targets. We also propose a novel data-driven technique to characterize unmodeled uncertainty, such as calibration errors, at the pixel level and show that this method ensures stability of the KF.
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