Angle-Aware Coverage with Camera Rotational Motion Control

TL;DR

This paper introduces a new control strategy for drone networks that optimizes both camera orientation and drone movement to improve 3D mapping quality, leveraging GPU acceleration for real-time performance.

Contribution

It proposes a novel angle-aware coverage control method with a QP-based controller and GPU acceleration, enhancing 3D reconstruction from aerial images.

Findings

Controller achieves real-time performance in simulations.

Outperforms conventional coverage control methods.

Enhanced 3D map quality demonstrated in ROS simulations.

Abstract

This paper presents a novel control strategy for drone networks to improve the quality of 3D structures reconstructed from aerial images by drones. Unlike the existing coverage control strategies for this purpose, our proposed approach simultaneously controls both the camera orientation and drone translational motion, enabling more comprehensive perspectives and enhancing the map's overall quality. Subsequently, we present a novel problem formulation, including a new performance function to evaluate the drone positions and camera orientations. We then design a QP-based controller with a control barrier-like function for a constraint on the decay rate of the objective function. The present problem formulation poses a new challenge, requiring significantly greater computational efforts than the case involving only translational motion control. We approach this issue technologically, namely by introducing JAX, utilizing just-in-time (JIT) compilation and Graphical Processing Unit (GPU) acceleration. We finally conduct extensive verifications through simulation in ROS (Robot Operating System) and show the real-time feasibility of the controller and the superiority of the present controller to the conventional method.