论文标题
使用固定翼无人机使用车载视觉的固定后导航
Post-Stall Navigation with Fixed-Wing UAVs using Onboard Vision
论文作者
论文摘要
最近的研究已通过使用直接非线性模型预测控制（NMPC），使固定翼无人机（UAV）能够在受约束空间中操纵。但是，这种方法仅限于先验的已知地图和地面真实状态测量。在本文中，我们提出了一种直接的NMPC方法，该方法利用Nanomap（一种轻巧的点云映射框架，用于使用板立体声视觉来生成无碰撞轨迹。我们首先探讨了我们的模拟方法，并证明我们的算法足以在城市环境中基于视觉的导航。然后，我们使用42英寸固定翼无人机演示了在硬件中的方法，并表明我们的运动计划算法能够使用简约的目标点在建筑物周围导航。我们还表明，存储点云历史记录对于导航这些类型的受限环境很重要。
Recent research has enabled fixed-wing unmanned aerial vehicles (UAVs) to maneuver in constrained spaces through the use of direct nonlinear model predictive control (NMPC). However, this approach has been limited to a priori known maps and ground truth state measurements. In this paper, we present a direct NMPC approach that leverages NanoMap, a light-weight point-cloud mapping framework to generate collision-free trajectories using onboard stereo vision. We first explore our approach in simulation and demonstrate that our algorithm is sufficient to enable vision-based navigation in urban environments. We then demonstrate our approach in hardware using a 42-inch fixed-wing UAV and show that our motion planning algorithm is capable of navigating around a building using a minimalistic set of goal-points. We also show that storing a point-cloud history is important for navigating these types of constrained environments.
