论文标题
APF-PF：3D反应性障碍物的概率深度感知
APF-PF: Probabilistic Depth Perception for 3D Reactive Obstacle Avoidance
论文作者
论文摘要
本文提出了一个在存在环境障碍的部分可观察性的情况下避免3D障碍物的框架。该方法着重于人工电位函数（APF）控制器的实用性，在实用环境中，有关接近性的嘈杂和不完整的信息是不可避免的。我们提出了一种粒子过滤器（PF）方法，以估计输入深度图像流中潜在的障碍位置。然后，可能的候选人被用于生成一个动作，该动作在每次瞬间时都会操纵机器人的电势梯度。当机器人对不正确的感知信息的敏感性时，对四摩托无人机的严格实验验证突出了该方法的鲁棒性和可靠性。所提出的感知和控制堆栈在无人机上运行，证明了实时应用程序和敏捷机器人的计算可行性。
This paper proposes a framework for 3D obstacle avoidance in the presence of partial observability of environment obstacles. The method focuses on the utility of the Artificial Potential Function (APF) controller in a practical setting where noisy and incomplete information about the proximity is inevitable. We propose a Particle Filter (PF) approach to estimate potential obstacle locations in an input depth image stream. The probable candidates are then used to generate an action that maneuvers the robot towards the negative gradient of potential at each time instant. Rigorous experimental validation on a quadrotor UAV highlights the robustness and reliability of the method when robot's sensitivity to incorrect perception information can be concerning. The proposed perception and control stack is run onboard the UAV, demonstrating the computational feasibility for real-time applications and agile robots.
