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This paper proposes a framework for 3D obstacle avoidance in the presence ofpartial observability of environment obstacles. The method focuses on theutility of the Artificial Potential Function (APF) controller in a practicalsetting where noisy and incomplete information about the proximity isinevitable. We propose a Particle Filter (PF) approach to estimate potentialobstacle locations in an input depth image stream. The probable candidates arethen used to generate an action that maneuvers the robot towards the negativegradient of potential at each time instant. Rigorous experimental validation ona quadrotor UAV highlights the robustness and reliability of the method whenrobot's sensitivity to incorrect perception information can be concerning. Theproposed perception and control stack is run onboard the UAV, demonstrating thecomputational feasibility for real-time applications and agile robots.

APF-PF: Probabilistic Depth Perception for 3D Reactive Obstacle Avoidance