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

TL;DR

This paper introduces a semi-autonomous coverage control method for 3D reconstruction that combines human intuition with autonomous drone navigation, improving model quality without operational conflicts.

Contribution

It presents a novel stealthy coverage control strategy that decouples drone motion from human navigation to enhance 3D reconstruction quality.

Findings

Semi-autonomous system outperforms fully autonomous in model quality

Decoupling drone motion from human control reduces operational conflicts

Simulation results validate the effectiveness of the proposed approach

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.