  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.
