Hybrid control for mobile target localization with stereo vision
Hybrid control for mobile target localization with stereo vision
复制标题
立体视觉移动目标定位的混合控制
DOI：
10.1109/cdc.2013.6760280
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
M. Zavlanos
中科院分区：
文献类型：
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作者：
Charles Freundlich;Philippos Mordohai;M. Zavlanos
在本文中，我们控制了一个移动立体相机的图像采集，该相机正在主动定位一组移动目标。特别是，假设至少有一对目标的立体图像可用，我们提出了一种新的方法来控制立体摄像机的旋转和平移，从而使目标的下一次观测最小化其定位不确定性。我们将此问题称为移动目标的次优视角问题（mNBV）。使用立体摄像机的优点是，通过三角测量，机器人在一次观察中同时拍摄的两张图像可以产生目标的距离和方位测量，以及它们的不确定性。当获得更多的测量值时，卡尔曼滤波器融合了完整的状态历史和协方差估计。我们的mNBV问题的解决方案确定了相机和目标之间的相对变换，从而使目标位置的融合不确定性最小化。我们确定了一个在尊重视野约束的情况下实现mNBV的运动计划。特别是，对于每一个新的观测，我们在相对于相机的帧中计算一个新的mNBV，随后通过梯度下降算法在全局坐标中实现该视图，该算法也尊重视场约束。将mNBV与运动规划相结合，形成了一个混合系统，并通过计算机仿真加以说明。
In this paper, we control image collection for a mobile stereo camera that is actively localizing a group of mobile targets. In particular, assuming that at least one pair of stereo images of the targets is available, we propose a novel approach to control the rotation and translation of the stereo camera so that the next observation of the targets will minimize their localization uncertainty. We call this problem the Next-Best-View problem for mobile targets (mNBV). The advantage of using a stereo camera is that, using triangulation, the two simultaneous images taken by the robot during a single observation can yield range and bearing measurements of the targets, as well as their uncertainty. A Kalman filter fuses the full state history and covariance estimates, as more measurements are acquired. Our solution to the mNBV problem determines the relative transformations between camera and targets that will minimize the fused uncertainty of the targets' locations. We determine a motion plan that realizes the mNBV while respecting field of view constraints. In particular, with every new observation, we compute a new mNBV in the frame relative to the camera and subsequently realize this view in global coordinates via a gradient descent algorithm that also respects field of view constraints. Integration of mNBV with motion planning results in a hybrid system, which we illustrate in computer simulations.