Sensor fusion for autonomous robotics in unstructured environments

非结构化环境中自主机器人的传感器融合

• 批准号: 195328341
• 负责人: Professor Dr.-Ing. Dietrich Paulus
• 金额: --
• 依托单位: Institut für Computervisualistik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2011
• 资助国家: 德国
• 起止时间: 2010-12-31 至 2016-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/195328341?language=en
• 关键词: Sensor; fusion; autonomous; robotics; unstructured

Terrain classification, navigation, and mapping for autonomous mobile system will be realized for the non-urban environments. The methods will work on data from 3D laser scanners, position and odometry sensors, GPS, and color cameras. By fusion of sensor data, additional information is integrated into the laser measurements and merged to create a semantic 3D map. The approach will be able to deal with a number of uncertainties in unstructured terrain, without need of map or route information. Therefore, the terrain must be analyzed and interpreted as exactly as possible in order to plan the best possible route.Detected obstacles are classified as static or dynamic objects, such as a forest, road or vehicle, and semantics as entered in the 3D map. Thus the autonomous vehicle can decide and plan a path for safe navigation in unknown terrain. The approach described is different from others as a complete fusion and sensor integration for navigation and creating a real-time semantic 3D map is implemented. The first extension to the existing projects provides for the creation of a semantic Markov random field for terrain classification. This will enable us to integrate detected objects in the environment of the robot into the classification process.This includes for example the knowledge, to drive a vehicles on roads or drivable surfaces as well as possible, which can be used for adapting the classification in areas in which other dynamic obstacles are found.The second extension is the process developed to create maps from 3D laser scans. New methods are developed, which allows the robot to maintain an existing map over a longer period of time, and to expand, even if the robot has been powered down and restarted. In addition, a new idea will be developed and tested to improve the robustness of the mapping, which maintains several fundamentally different hypotheses about the course of the trajectory. Especially not now not meant to be considered only one probable trajectory. Thus, it is possible to make better decisions about the data association.

在非城市环境中实现自主移动系统的地形分类、导航和制图。这些方法将处理来自3D激光扫描仪、位置和里程计传感器、GPS和彩色相机的数据。通过融合传感器数据，将附加信息集成到激光测量中并合并以创建语义三维地图。该方法将能够在不需要地图或路线信息的情况下处理非结构化地形中的许多不确定因素。因此，必须尽可能准确地分析和解释地形，以便规划最佳路线。检测到的障碍物被分类为静态或动态对象，如森林、道路或车辆，并在3D地图中输入语义。因此，自动驾驶车辆可以在未知地形中确定和规划安全导航的路径。与其他方法不同的是，该方法实现了导航和创建实时语义3D地图的完整融合和传感器集成。对现有项目的第一个扩展提供了用于地形分类的语义马尔可夫随机场的创建。这将使我们能够将机器人环境中检测到的物体整合到分类过程中。例如，这包括在道路或可驾驶路面上驾驶车辆的知识，这些知识可以用于在发现其他动态障碍物的区域调整分类。第二个扩展是开发从3D激光扫描创建地图的过程。开发了新的方法，使机器人能够在更长的时间内维持现有的地图，并且即使机器人已经断电并重新启动，也可以扩展。此外，将开发和测试一个新的想法，以提高映射的鲁棒性，它保留了关于轨迹过程的几个根本不同的假设。尤其是现在不应该只考虑一种可能的轨迹。因此，可以对数据关联做出更好的决策。

项目成果

Semantic mapping for mobile outdoor robots

移动户外机器人的语义映射

• DOI: 10.1109/mva.2015.7153196
• 发表时间: 2015
• 期刊: 2015 14th IAPR International Conference on Machine Vision Applications (MVA)
• 作者: Dagmar ;Friedmann;Susanne ;Hedrich;Paulus;Dietrich
• 通讯作者: Dietrich