Statistical Methods for Collaborative Multi-Robot Mapping

IIS-9877033Thrun, SebastianCarnegie Mellon University, 69,602 - 12美元。合作多机器人映射的统计方法这是一个为期三年的持续奖励的第一年资助。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共同获取室内环境的地图。迄今为止，大多数成功的移动机器人应用程序都依赖于地图；然而，用一个或多个机器人自动获取这类地图的问题在很大程度上仍未解决。为了建立这样的地图，必须克服三个不确定性来源：传感的不确定性（例如，用测距仪），机器人里程计的不确定性，以及不同机器人相对于彼此的初始姿态的不确定性。此外，机器人必须整合分散收集的信息，并协调它们的探索策略。这项研究基于一个统一的统计框架，该框架是由PI和其他人最近设计的。该框架将构建地图的问题作为最大可能性问题，即根据机器人团队获得的数据找到最可能的地图。它使用有效的统计技术（特别是Dempster的EM算法）来实现可能性最大化。该项目在几个方面超越了之前的工作：首先，它将解决基于团队的地图获取问题，机器人必须确定彼此之间的相对位置才能构建一张地图。这将通过在基本统计框架中引入额外的自由度来实现。其次，地图将由原子物体形状（如矩形、圆形或其分段）的连词来表示，而不是网格或类似的非图标表示。为了估计这些物体的总数、大小和姿态，该项目将采用快速的、基于采样的算法。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。这将通过最大化信息增益的方法来实现，通过预期的相对熵来测量。最后，本项目将对动态环境下的学习地图进行探索性研究。如果成功，该项目有望为移动机器人测绘带来一个新的、更强大的、可扩展的算法家族。

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.