Learning 3-Dimensional Maps of Unstructured Environments on a Mobile Robot.

该项目涉及学习的三维表示的非结构化的室内环境的自主移动的机器人。映射的一般问题是机器人学中的一个基本问题，因为映射对于任何重要使命的成功至关重要。在过去，大量的努力致力于构建二维地图，如平面图。这些方法简化了问题，但它们发展得很好，足以解决基本任务。但移动的机器人越来越多地在三维空间中操作。这是由于运动的显著进步以及在任何现实环境中克服例如斜坡或楼梯的实际需要。我们提出了一种在线学习算法，它创建了一个度量3D表示编码的虚拟现实建模语言（VRML）的非结构化环境。该系统以真实的世界为目标，即，它必须科普机器人的不可靠和嘈杂的导航和距离传感器数据。该算法采用了一种进化的方法，其中一个VRML邻域模型是实时提取的本地三维占用网格，而机器人沿沿着移动。受以前成功的3D眼手协调与机器人手臂的实时学习工作的启发，进化算法试图生成再现大量传感器数据的VRML代码。为此，我们使用了所谓的邻域原则。粗略地说，邻域原则使用了机器人在世界上位置的概率估计，称为立场，这大大降低了学习步骤的复杂性。作为演示场景，该系统将在IUB救援机器人上实现。

The project deals with learning of a three dimensional representation of an unstructured indoor environment by an autonomous mobile robot. The general problem of mapping is a fundamental issue in robotics, as maps are crucial for the success of any non-trivial mission. In the past, significant efforts were devoted to build two dimensional maps like floorplans. These approaches simplify the problem, but they are well developed and they are sufficient for solving basic tasks. But mobile robots increasingly operate in three dimensions. This is due to significant advances in locomotion and the actual need to overcome for example slopes or stairs in any realistic environment. We propose an on-line learning algorithm, which creates a metric 3D representation encoded in the Virtual Reality Modeling Language (VRML) of an unstructured environment. The system is targeted for the real world, i.e., it has to cope with the unreliable and noisy navigation- and range-sensor data of a robot. The algorithm uses an evolutionary method where a VRML neighborhood model is extracted in realtime from a local 3D occupancy grid while the robot moves along. Inspired by previous successful work on realtime learning of 3D eye-hand coordination with a robot-arm, the evolutionary algorithm tries to generate VRML code that reproduce the vast amounts of sensor data. In doing so, we use a so-called neighborhood principle. Roughly speaking, the neighborhood principle uses a probabilistic estimation of the robots position in the world called the standpoint, which greatly reduces the complexity of learning steps. As demonstrator scenario, the system will be implemented on the IUB rescue robots.