UAV Active Perception and Motion Control for Improving Navigation Using Low-Cost Sensors

TL;DR

This paper presents a model pipeline combining computer vision and control methods with low-cost sensors to enable perception-aware motion control for UAV navigation and object detection in simulation.

Contribution

It introduces a novel active perception system integrating YOLOv8 and a CNN called ActivePerceptionNet for improved object localization and distance estimation.

Findings

Efficient object height and distance estimation achieved

Effective localization demonstrated in simulation

Active perception improves UAV navigation accuracy

Abstract

In this study a model pipeline is proposed that combines computer vision with control-theoretic methods and utilizes low cost sensors. The proposed work enables perception-aware motion control for a quadrotor UAV to detect and navigate to objects of interest such as wind turbines and electric towers. The distance to the object of interest was estimated utilizing RGB as the primary sensory input. For the needs of the study, the Microsoft AirSim simulator was used. As a first step, a YOLOv8 model was integrated providing the basic position setpoints towards the detection. From the YOLOv8 inference, a target yaw angle was derived. The subsequent algorithms, combining performant in computational terms computer vision methods and YOLOv8, actively drove the drone to measure the height of the detection. Based on the height, an estimate of the depth was retrieved. In addition to this step, a convolutional neural network was developed, namely ActvePerceptionNet aiming at active YOLOv8 inference. The latter was validated for wind turbines where the rotational motion of the propeller was found to affect object confidence in a near periodical fashion. The results of the simulation experiments conducted in this study showed efficient object height and distance estimation and effective localization.