Local Perception for the Autonomous Navigation of Multicopters

多旋翼飞行器自主导航的局部感知

• 批准号: 200547885
• 负责人: Professor Dr. Sven Behnke
• 金额: --
• 依托单位: Institut für Informatik
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2011
• 资助国家: 德国
• 起止时间: 2010-12-31 至 2019-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/200547885?language=en
• 关键词: Local; Perception; Autonomous; Navigation; Multicopters

Objective of the proposed project is the generation of an environment representation for an autonomous copter which allows for a safe 3D navigation and obstacle avoidance (in P3). This representation is based on the pose estimate of the copter (P1) and measurements of onboard distance sensors and cameras. In addition to the 3D laser range scanner, which has been developed in the first project phase and ultrasonic distance sensors, further modalities shall be integrated: radar sensors and time-of-flight cameras. The obstacles that are detected in the existing multi-camera system will be incorporated more, in particular visual object point, which are generated in P4 by stereo triangulation and bundle adjustment, and semi-dense visual obstacles from P5. The detection of obstacles must work reliably, even when individual modalities fail, e.g. due to the obstacle properties or the lighting conditions. The requirements for the environment representation are derived from navigation planning. In order to increase the level of autonomy of the copter, not only egocentric representations with relative precision will be created onboard, but also allocentric maps.The egocentric map will be maintained with high frequency by registering the most recent measurements of Laserscanner and cameras. The registration of all measurements will be optimized globally to generate an allocentric environment representation. For this, the GNNS-Pose from P1 will be incorporated. The calibration of the multimodal sensor system will be continuously refined by minimizing registration errors. Registration will be performed by graph optimization, for which we will develop new methods for the simultaneous registration of multiple modalities. We will also work on the modelling of dynamic obstacles. These will be separated from the environment and modeled separately. This is needed for motion prediction - the basis for anticipatory navigation planning in P3. Furthermore, we will create in cooperation with P7 a semantic categorization of the environment. Surfaces will be assigned to navigation-relevant categories like floor, facade, roof, vegetation and relevant objects like persons, vehicles, and windows will be detected. To this end, we will advance methods for 3D fusion of semantic categorization, object detection, and learning from few annotated examples.

拟议项目的目标是为自主直升机生成环境表示，以实现安全的 3D 导航和避障（在 P3 中）。该表示基于直升机 (P1) 的位姿估计以及机载距离传感器和摄像机的测量。除了在第一阶段开发的 3D 激光测距扫描仪和超声波距离传感器之外，还应集成更多模式：雷达传感器和飞行时间相机。现有多摄像机系统中检测到的障碍物将被更多地纳入其中，特别是P4中通过立体三角测量和束平差生成的视觉物点，以及P5中的半密集视觉障碍物。即使个别模式失败，例如障碍物检测也必须可靠地工作。由于障碍物特性或照明条件。环境表示的要求源自导航规划。为了提高直升机的自主水平，不仅将在机上创建具有相对精度的自我中心表示，而且还将创建非中心地图。自我中心地图将通过记录激光扫描仪和相机的最新测量结果进行高频维护。所有测量的注册将在全球范围内进行优化，以生成异中心环境表示。为此，将合并 P1 的 GNNS-Pose。多模态传感器系统的校准将通过最小化配准误差来不断完善。配准将通过图形优化来执行，为此我们将开发用于同时配准多种模式的新方法。我们还将致力于动态障碍物的建模。这些将与环境分离并单独建模。这是运动预测所必需的 - P3 中预期导航规划的基础。此外，我们将与 P7 合作创建环境的语义分类。表面将被分配给与导航相关的类别，如地板、立面、屋顶、植被，并且将检测人员、车辆和窗户等相关物体。为此，我们将推进语义分类、对象检测和从一些带注释的示例中学习的 3D 融合方法。

项目成果

Multilayered Mapping and Navigation for Autonomous Micro Aerial Vehicles

• DOI: 10.1002/rob.21603
• 发表时间: 2016-06-01
• 期刊: JOURNAL OF FIELD ROBOTICS
• 影响因子: 8.3
• 作者: Droeschel, David;Nieuwenhuisen, Matthias;Behnke, Sven
• 通讯作者: Behnke, Sven

Semi-supervised Semantic Mapping Through Label Propagation with Semantic Texture Meshes

• DOI: 10.1007/s11263-019-01187-z
• 发表时间: 2019-06
• 期刊: International Journal of Computer Vision
• 影响因子: 19.5
• 作者: R. Rosu;Jan Quenzel;Sven Behnke

Online depth calibration for RGB-D cameras using visual SLAM

• DOI: 10.1109/iros.2017.8206043
• 发表时间: 2017-09
• 期刊: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Jan Quenzel;R. Rosu;Sebastian Houben;Sven Behnke

Keyframe-Based Photometric Online Calibration and Color Correction

基于关键帧的光度在线校准和色彩校正

• DOI: 10.1109/iros.2018.8593595
• 发表时间: 2018
• 期刊: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Quenzel;Houben;Behnke
• 通讯作者: Behnke