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Angle-Aware Coverage with Camera Rotational Motion Control

Zhiyuan Lu, Muhammad Hanif, Takumi Shimizu, Takeshi Hatanaka

TL;DR

This work tackles high-quality 3D map reconstruction via UAVs by extending angle-aware coverage to jointly control drone motion and camera orientation. It introduces a 5D observation framework, a Gaussian performance function, and an objective decay constraint, solved with a QP-based controller that enforces a control-barrier-like condition on the rate of improvement. To meet real-time demands, the authors implement JAX with JIT compilation and GPU acceleration, validating the approach through ROS simulations and demonstrating improved coverage over prior angle-fixed methods. The findings highlight substantial practical impact for efficient multi-robot view planning in SfM pipelines, with future work focusing on hardware experiments and reconstruction accuracy assessments.

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.

Angle-Aware Coverage with Camera Rotational Motion Control

TL;DR

This work tackles high-quality 3D map reconstruction via UAVs by extending angle-aware coverage to jointly control drone motion and camera orientation. It introduces a 5D observation framework, a Gaussian performance function, and an objective decay constraint, solved with a QP-based controller that enforces a control-barrier-like condition on the rate of improvement. To meet real-time demands, the authors implement JAX with JIT compilation and GPU acceleration, validating the approach through ROS simulations and demonstrating improved coverage over prior angle-fixed methods. The findings highlight substantial practical impact for efficient multi-robot view planning in SfM pipelines, with future work focusing on hardware experiments and reconstruction accuracy assessments.

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.
Paper Structure (12 sections, 1 theorem, 22 equations, 7 figures, 1 table)

This paper contains 12 sections, 1 theorem, 22 equations, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Problem settings
  3. Drones, Virtual Field, and Geometry
  4. Performance function and importance index
  5. Objective function
  6. QP-based Controller Design
  7. Computation acceleration
  8. JIT compilation
  9. GPU acceleration
  10. Controller Evaluation and Verification
  11. Conclusion
  12. Proof of Theorem \ref{['th:qp_form']}

Key Result

Theorem 3.1

Suppose that no $q_j (j \in \mathcal{M})$ is located on the boundary of $\mathcal{V}_i(p)$. When $\alpha_1: \mathbb{R} \rightarrow \mathbb{R}$ and $\alpha_2: \mathbb{R} \rightarrow \mathbb{R}$ are set as a linear function such that $\alpha_1(b_{i,I})=a_1 b_{i,I}$ and $\alpha_2(b_{i,\phi})=a_2 b_{i,\ where

Figures (7)

  • Figure 1: Collaborative 3D map reconstruction using drone networks scene.
  • Figure 2: Gimbal coordinate reference.
  • Figure 3: Illustration of the angle aware coverage problem with camera orientation control.
  • Figure 4: Controller architecture
  • Figure 5: Comparisons of CPU and GPU computational time.
  • ...and 2 more figures

Theorems & Definitions (3)

  • Remark 1
  • Theorem 3.1
  • Remark 2