论文标题
具有动态高斯过程的主动和互动映射，用于移动操纵器的隐式表面
Active and Interactive Mapping with Dynamic Gaussian Process Implicit Surfaces for Mobile Manipulators
论文作者
论文摘要
在这封信中，我们提出了一个交互式概率映射框架，用于从堆中挑选对象的移动操纵器。目的是映射场景，积极决定下一步去哪里以及选择哪个对象，通过选择所选的对象对场景进行更改，然后与旁边映射这些更改。提出的框架使用一种新型的动态高斯过程（GP）隐式表面方法来逐步构建和更新反映环境变化的场景图。积极地，该框架提供了下一最佳的视角，平衡了选择对象可及性的需求与地图信息增益（IG）。为了优先访问边界段以优先于未知区域，IG公式通过利用GP内核衍生物来包括基于不确定性梯度的边界得分。这导致了一种有效的策略，该策略解决了未知环境探索和对象拾取剥削的经常冲突的要求，但给定有限的执行范围。我们通过软件仿真和现实生活实验证明了框架的有效性。
In this letter, we present an interactive probabilistic mapping framework for a mobile manipulator picking objects from a pile. The aim is to map the scene, actively decide where to go next and which object to pick, make changes to the scene by picking the chosen object, and then map these changes alongside. The proposed framework uses a novel dynamic Gaussian Process (GP) Implicit Surface method to incrementally build and update the scene map that reflects environment changes. Actively the framework provides the next-best-view, balancing the need for picking object reachability with map information gain (IG). To enforce a priority of visiting boundary segments over unknown regions, the IG formulation includes an uncertainty gradient-based frontier score by exploiting the GP kernel derivative. This leads to an efficient strategy that addresses the often conflicting requirement of unknown environment exploration and object picking exploitation given a limited execution horizon. We demonstrate the effectiveness of our framework with software simulation and real-life experiments.
