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Stealthy Coverage Control for Human-enabled Real-Time 3D Reconstruction

Reiji Terunuma, Yuta Nakamura, Takuma Abe, Takeshi Hatanaka

TL;DR

The paper addresses spatially heterogeneous data requirements for high-quality 3D reconstruction by integrating a human-in-the-loop with autonomous drone sampling. It introduces stealthy coverage control, which expresses human intent in coverage decisions while decoupling drone motion from human navigation via a matrix $A(g)$, ensuring the operator’s perception of the average state remains unaffected. The approach combines a constrained quadratic program for autonomous sampling with geometry-based sensing metrics $f_1$ and $f_2$, and an importance index $\phi_j$ to drive exploration toward under-sampled regions identified by the human. Simulation in a Unity/ROS2 environment with $n=3$ drones demonstrates that human-guided sampling improves final mesh quality and reduces artifacts compared to fully autonomous coverage control, highlighting the practical impact of human-in-the-loop planning for complex scenes.

Abstract

In this paper, we propose a novel semi-autonomous image sampling strategy, called stealthy coverage control, for human-enabled 3D structure reconstruction. The present mission involves a fundamental problem: while the number of images required to accurately reconstruct a 3D model depends on the structural complexity of the target scene to be reconstructed, it is not realistic to assume prior knowledge of the spatially non-uniform structural complexity. We approach this issue by leveraging human flexible reasoning and situational recognition capabilities. Specifically, we design a semi-autonomous system that leaves identification of regions that need more images and navigation of the drones to such regions to a human operator. To this end, we first present a way to reflect the human intention in autonomous coverage control. Subsequently, in order to avoid operational conflicts between manual control and autonomous coverage control, we develop the stealthy coverage control that decouples the drone motion for efficient image sampling from navigation by the human. Simulation studies on a Unity/ROS2-based simulator demonstrate that the present semi-autonomous system outperforms the one without human interventions in the sense of the reconstructed model quality.

Stealthy Coverage Control for Human-enabled Real-Time 3D Reconstruction

TL;DR

The paper addresses spatially heterogeneous data requirements for high-quality 3D reconstruction by integrating a human-in-the-loop with autonomous drone sampling. It introduces stealthy coverage control, which expresses human intent in coverage decisions while decoupling drone motion from human navigation via a matrix , ensuring the operator’s perception of the average state remains unaffected. The approach combines a constrained quadratic program for autonomous sampling with geometry-based sensing metrics and , and an importance index to drive exploration toward under-sampled regions identified by the human. Simulation in a Unity/ROS2 environment with drones demonstrates that human-guided sampling improves final mesh quality and reduces artifacts compared to fully autonomous coverage control, highlighting the practical impact of human-in-the-loop planning for complex scenes.

Abstract

In this paper, we propose a novel semi-autonomous image sampling strategy, called stealthy coverage control, for human-enabled 3D structure reconstruction. The present mission involves a fundamental problem: while the number of images required to accurately reconstruct a 3D model depends on the structural complexity of the target scene to be reconstructed, it is not realistic to assume prior knowledge of the spatially non-uniform structural complexity. We approach this issue by leveraging human flexible reasoning and situational recognition capabilities. Specifically, we design a semi-autonomous system that leaves identification of regions that need more images and navigation of the drones to such regions to a human operator. To this end, we first present a way to reflect the human intention in autonomous coverage control. Subsequently, in order to avoid operational conflicts between manual control and autonomous coverage control, we develop the stealthy coverage control that decouples the drone motion for efficient image sampling from navigation by the human. Simulation studies on a Unity/ROS2-based simulator demonstrate that the present semi-autonomous system outperforms the one without human interventions in the sense of the reconstructed model quality.
Paper Structure (9 sections, 1 theorem, 27 equations, 10 figures)

This paper contains 9 sections, 1 theorem, 27 equations, 10 figures.

Table of Contents

  1. Scenario and System Architecture
  2. Environment and Drone Dynamics
  3. Human-enabled System Architecture
  4. Coverage Control
  5. Stealthy Coverage Control
  6. Simulation
  7. Demonstration of Stealthy Control
  8. Demonstration of 3D Reconstruction
  9. Conclusion

Key Result

Theorem 1

Consider the system in Definition def:stealth_control. Denote $i$-th column of $\bm1_{n}\otimes I_{3} \in \mathbb{R}^{3n\times 3}$ and $\left(\dfrac{\partial}{\partial \theta}\overline{\mathrm{Dir}(g)}\right)^\top$ by $e_i^p$ and $e_i^\theta({g})\in \mathbb{R}^{2n\times 3}$, respectively. Suppose th Then, the input (eq:stealth_control_input) constitutes a stealthy control.

Figures (10)

  • Figure 1: Illustration of the target scenario, where multiple drones sample aerial images of the set $\mathcal{Q}$ to reconstruct a 3D model of a scene.
  • Figure 2: Illustration of the intended human-robots interactions. The human wears a head-mounted display (HMD), visually perceives the reconstructed 3D model and the average state of the drones in the virtual space, and determines velocity commands to control the virtual average drone.
  • Figure 3: Geometric relation between a drone with state $g_{i}$ and an observation point $h_j$.
  • Figure 4: System architecture of the present semi-autonomous image sampling control. The left block shows stealthy coverage control, real-time structure reconstruction through NeuralRecon, and human intervention, all implemented on ROS2. The right block shows the virtual 3D scene built on Unity.
  • Figure 5: Comparison of the trajectories of the average position for a circular path with and without the stealthy control.
  • ...and 5 more figures

Theorems & Definitions (2)

  • Definition 1
  • Theorem 1