Asynchronous Collaborative Autoscanning with Mode Switching for Multi-Robot Scene Reconstruction

TL;DR

This paper introduces an asynchronous multi-robot autoscanning approach with mode switching between exploration and reconstruction, optimizing task assignment to improve indoor scene reconstruction efficiency and quality.

Contribution

It proposes a novel asynchronous collaborative autoscanning framework with mode switching and task-flow modeling, optimizing task assignment via a modified MDMTSP for better efficiency and quality.

Findings

Outperforms previous methods in scanning efficiency.

Achieves higher reconstruction quality.

Effective task assignment and mode switching enhance collaboration.

Abstract

When conducting autonomous scanning for the online reconstruction of unknown indoor environments, robots have to be competent at exploring scene structure and reconstructing objects with high quality. Our key observation is that different tasks demand specialized scanning properties of robots: rapid moving speed and far vision for global exploration and slow moving speed and narrow vision for local object reconstruction, which are referred as two different scanning modes: explorer and reconstructor, respectively. When requiring multiple robots to collaborate for efficient exploration and fine-grained reconstruction, the questions on when to generate and how to assign those tasks should be carefully answered. Therefore, we propose a novel asynchronous collaborative autoscanning method with mode switching, which generates two kinds of scanning tasks with associated scanning modes, i.e., exploration task with explorer mode and reconstruction task with reconstructor mode, and assign them to the robots to execute in an asynchronous collaborative manner to highly boost the scanning efficiency and reconstruction quality. The task assignment is optimized by solving a modified Multi-Depot Multiple Traveling Salesman Problem (MDMTSP). Moreover, to further enhance the collaboration and increase the efficiency, we propose a task-flow model that actives the task generation and assignment process immediately when any of the robots finish all its tasks with no need to wait for all other robots to complete the tasks assigned in the previous iteration. Extensive experiments have been conducted to show the importance of each key component of our method and the superiority over previous methods in scanning efficiency and reconstruction quality.