论文标题
主动姿势估计的分层方法
A Hierarchical Approach to Active Pose Estimation
论文作者
论文摘要
创建能够在大环境中找到和操纵对象的移动机器人是一个活跃的研究主题。这些机器人不仅需要能够搜索特定对象，而且还需要估计它们的姿势通常依赖于环境观测值，这在闭塞存在下更加困难。因此，为了解决这个问题，我们提出了一种简单的分层方法来估计所需物体的姿势。使用RGB图像运行的主动视觉搜索模块首先获得对象2D姿势的粗略估计，然后使用点云数据进行更昂贵的活动姿势估计模块。我们从经验上表明，处理图像特征以获得更丰富的观察会加快搜索和构成估计计算的速度，与指示对象是否在当前图像中的二进制决策相比。
Creating mobile robots which are able to find and manipulate objects in large environments is an active topic of research. These robots not only need to be capable of searching for specific objects but also to estimate their poses often relying on environment observations, which is even more difficult in the presence of occlusions. Therefore, to tackle this problem we propose a simple hierarchical approach to estimate the pose of a desired object. An Active Visual Search module operating with RGB images first obtains a rough estimation of the object 2D pose, followed by a more computationally expensive Active Pose Estimation module using point cloud data. We empirically show that processing image features to obtain a richer observation speeds up the search and pose estimation computations, in comparison to a binary decision that indicates whether the object is or not in the current image.
