论文标题
在机器人本地化的信念空间中行动，感知和计划
Act, Perceive, and Plan in Belief Space for Robot Localization
论文作者
论文摘要
在本文中，我们概述了一种交错的表演和规划技术，以快速降低估计机器人姿势的不确定性，通过感知来自环境的相关信息，因为它识别对象或要求某人寻求方向。 通常，现有的本地化方法依赖于低水平的几何特征，例如点，线条和平面，而这些方法则提供了所需的准确性，但它们可能需要时间来收敛，尤其是在不正确的初始猜测中。在我们的方法中，任务计划者计算一系列动作和感知任务，以从机器人的感知系统中积极获取相关信息。我们在较大的状态空间中验证了我们的方法，以说明方法尺度和实际环境中如何显示我们方法在真实机器人上的适用性。 我们证明，在实际情况下，我们的方法是合理的，概率的，并且可以处理。
In this paper, we outline an interleaved acting and planning technique to rapidly reduce the uncertainty of the estimated robot's pose by perceiving relevant information from the environment, as recognizing an object or asking someone for a direction. Generally, existing localization approaches rely on low-level geometric features such as points, lines, and planes, while these approaches provide the desired accuracy, they may require time to converge, especially with incorrect initial guesses. In our approach, a task planner computes a sequence of action and perception tasks to actively obtain relevant information from the robot's perception system. We validate our approach in large state spaces, to show how the approach scales, and in real environments, to show the applicability of our method on real robots. We prove that our approach is sound, probabilistically complete, and tractable in practical cases.
