论文标题
实时四项导航，通过在非结构化环境中计划深度空间进行计划
Real-time Quadrotor Navigation Through Planning in Depth Space in Unstructured Environments
论文作者
论文摘要
本文解决了四型无人机的非结构化环境中基于实时视觉的自主障碍避免的问题。我们假设我们的无人机配备了一个前面的立体声摄像头，它是唯一感知周围世界的传感器。此外，所有计算均在车载上执行。可行的轨迹产生这种问题需要快速碰撞检查以及有效的计划算法。我们在深度图像空间中提出了一种轨迹生成方法，该方法指的是深度图像所描绘的环境信息。为了预测向后的机器人轨迹中的碰撞，我们从沿路径的机器人姿势序列创建深度图像。我们将这些图像与通过前面的立体声摄像机感知到现实世界的深度图像进行比较。我们旨在在深度图像空间内产生燃料最佳轨迹。如果发生预测的碰撞，则使用切换策略来积极地偏离障碍物。为此，我们使用基于线性二次调节器（LQR）目标函数的两个闭环运动原始图。通过模拟和硬件实验验证了所提出的方法。
This paper addresses the problem of real-time vision-based autonomous obstacle avoidance in unstructured environments for quadrotor UAVs. We assume that our UAV is equipped with a forward facing stereo camera as the only sensor to perceive the world around it. Moreover, all the computations are performed onboard. Feasible trajectory generation in this kind of problems requires rapid collision checks along with efficient planning algorithms. We propose a trajectory generation approach in the depth image space, which refers to the environment information as depicted by the depth images. In order to predict the collision in a look ahead robot trajectory, we create depth images from the sequence of robot poses along the path. We compare these images with the depth images of the actual world sensed through the forward facing stereo camera. We aim at generating fuel optimal trajectories inside the depth image space. In case of a predicted collision, a switching strategy is used to aggressively deviate the quadrotor away from the obstacle. For this purpose we use two closed loop motion primitives based on Linear Quadratic Regulator (LQR) objective functions. The proposed approach is validated through simulation and hardware experiments.
