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

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 when inter-robot views overlap, producing temporally inconsistent, duplicated surfaces that degrade point-cloud reconstruction accuracy, 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 and real-world experiments, SPACE demonstrated superior performance over state-of-the-art approaches in both exploration and mapping metrics, demonstrating significant mitigation of the ghosting effects by multiple magnitudes.