SPACE: 3D Spatial Co-operation and Exploration Framework for Robust Mapping and Coverage with Multi-Robot Systems

TL;DR

SPACE is a semi-distributed framework that improves multi-robot indoor mapping and exploration by addressing overlapping view issues and enhancing frontier detection, leading to better coverage and map quality.

Contribution

The paper introduces a novel spatial cooperation framework with geometric techniques and adaptive frontier assignment for robust multi-robot indoor mapping.

Findings

SPACE outperforms existing methods in exploration efficiency.

Enhanced map accuracy and coverage in simulated environments.

Effective handling of overlapping views and frontier selection.

Abstract

In indoor environments, multi-robot visual (RGB-D) mapping and exploration hold immense potential for application in domains such as domestic service and logistics, where deploying multiple robots in the same environment can significantly enhance efficiency. However, there are two primary challenges: (1) the "ghosting trail" effect, which occurs due to overlapping views of robots impacting the accuracy and quality of point cloud reconstruction, and (2) the oversight of visual reconstructions in selecting the most effective frontiers for exploration. Given these challenges are interrelated, we address them together by proposing a new semi-distributed framework (SPACE) for spatial cooperation in indoor environments that enables enhanced coverage and 3D mapping. SPACE leverages geometric techniques, including "mutual awareness" and a "dynamic robot filter," to overcome spatial mapping constraints. Additionally, we introduce a novel spatial frontier detection system and map merger, integrated with an adaptive frontier assigner for optimal coverage balancing the exploration and reconstruction objectives. In extensive ROS-Gazebo simulations, SPACE demonstrated superior performance over state-of-the-art approaches in both exploration and mapping metrics.