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

arXiv: https://arxiv.org/abs/2010.08063 · HTML: https://mlsanity.com/paper/2010.08063/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge](https://mlsanity.com/paper/2103.11470/) (2021)
- [Robust Robotic Exploration and Mapping Using Generative Occupancy Map Synthesis](https://mlsanity.com/paper/2506.20049/) (2025)

## Abstract (arXiv)

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.