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.

翻译：在室内环境中，多机器人视觉（RGB-D）建图与探索在家庭服务和物流等领域具有巨大的应用潜力，在同一环境中部署多个机器人可显著提升效率。然而存在两大主要挑战：（1）“重影轨迹”效应，该效应因机器人视野重叠而影响点云重建的精度与质量；（2）视觉重建在选择最优探索边界时存在局限性。鉴于这些挑战相互关联，我们通过提出一种新型半分布式框架（SPACE）来共同解决这些问题，该框架支持室内环境中的空间协作，从而实现增强的覆盖与三维建图。SPACE利用几何技术（包括“相互感知”和“动态机器人滤波器”）来克服空间建图的约束。此外，我们提出了一种创新的空间边界检测系统与地图融合器，并结合自适应边界分配器以实现探索与重建目标间的最优覆盖平衡。在大量ROS-Gazebo仿真实验中，SPACE在探索与建图各项指标上均展现出优于现有先进方法的性能。