Statistical Methods for Collaborative Multi-Robot Mapping

摘要IIS-9877033Thrun，Sebastian Carnegie Mellon University$69,602-12 MOST。协作多机器人映射的统计方法这是为期三年的连续奖项的第一年资助。该项目将设计一系列新的统计算法，使移动机器人团队能够联合获取室内环境地图。到目前为止，大多数成功的移动机器人应用程序都依赖于地图；然而，使用一个或多个机器人自动获取此类地图的问题在很大程度上仍然没有解决。要建立这样的地图，必须克服三个不确定性来源：传感的不确定性(例如，使用测距仪)，机器人里程数的不确定性，以及不同机器人相对于彼此的初始姿势的不确定性。此外，机器人必须整合分布式收集的信息，并且它们必须协调它们的探索战略。这项研究基于PI和其他人最近设计的统一统计框架。这个框架将构建地图的问题作为一个最大似然问题提出，即在给定一组机器人获得的数据的情况下找到最可能的地图。它使用有效的统计技术(特别是Dempster的EM算法)来实现似然最大化。该项目在几个方面超越了以前的工作：首先，它将解决基于团队的地图获取问题，即机器人必须确定它们彼此之间的相对位置，以便构建单一地图。这将通过在基本统计框架中引入更多自由度来实现。其次，地图将由原子对象形状(例如，矩形、圆形或其分段)的合取来表示，而不是网格或类似的非图标表示。为了估计这类物体的总数、大小和姿势，该项目将应用基于采样的快速算法。第三，该项目寻求设计算法，使机器人团队能够协作探索环境。这将通过以预期相对熵衡量的最大化信息增益的方法来实现。最后，本项目将对动态环境中的地图学习进行探索性研究。如果成功，该项目有望带来一系列新的、更健壮和可扩展的移动机器人地图绘制算法。

Abstract IIS-9877033Thrun, SebastianCarnegie Mellon University $69,602 - 12 mos.STATISTICAL METHODS FOR COOPERATIVE MULTI-ROBOT MAPPINGThis is the first year funding of a three year continuing award. This project will devise a new family of statistical algorithms that enable teams of mobile robots to jointly acquire maps of indoor environments. The majority of successful mobile robot applications todate rely on maps; yet, the problem of automatically acquiring such maps with one or more robots remains largely unsolved. To build such maps, three sources of uncertainty must be overcome: uncertainty in sensing (e.g., with range finders), uncertainty in robot odometry, and uncertainty in the initial poses of the different robots relative to each other. In addition, robots must integrate information gathered distributedly, and they must coordinate their exploration strategies.The research is based on a unifying statistical framework, recently devised by the PI and others. This framework poses the problem of building maps as a maximum likelihood problem, of finding the most likely map given the data acquired by a team of robots. It uses efficient statistical techniques (specifically Dempster's EM algorithm) for likelihood maximization. The project goes beyond previous work in several aspects: First, it will address the problem of team-based map acquisition, where robots have to determine their relative location to each other in order to build a single map. This will be achieved by introducing additional degrees of freedom into the basic statistical framework. Second, maps will be represented by conjunctions of atomic object shapes (eg, rectangles, circles or segments thereof), instead of grids or similar non-iconic representations. To estimate the total number, sizes and poses of suchobjects, the project will apply fast, sampling-based algorithms. Third, the project seeks to devise algorithms that enable teams of robots to collaboratively explore an environment. This will be achieved by methods for maximizing information gain, as measured by expected relative entropy. Finally, this project will pursue explorative research on learning maps in dynamic environments. If successful, this project is expected to lead to a new, more robust and scalable family of algorithms for mobile robot mapping.