多机器人协同探索中解决重叠视图与遗漏区域问题，提升室内三维建图效率。
SPACE: 3D Spatial Co-operation and Exploration Framework for Robust Mapping and Coverage with Multi-Robot Systems
- 通过互知机制和动态机器人过滤，减少重叠视图对点云重建的干扰。
- 在仿真中实现更高覆盖率与更优建图质量，优于现有先进方法。
- 适合需要高效协同作业的智能服务与物流场景。
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应用潜力，多个机器人协同工作可显著提升效率。然而存在两大挑战：一是由于机器人视角重叠导致的‘鬼影轨迹’现象，影响点云重建的准确性和质量；二是探索过程中对视觉重建结果考虑不足，导致前沿选择不够优化。针对这两个相互关联的问题，我们提出一种新的半分布式框架SPACE，用于室内环境下的空间协作，实现更优的覆盖与三维建图。SPACE利用几何技术，包括‘互知’机制和‘动态机器人过滤’，克服空间建图限制。此外，我们设计了一种新型空间前沿检测系统与地图融合方法，并结合自适应前沿分配器，实现探索与重建目标间的平衡。在大量ROS-Gazebo仿真中，SPACE在探索与建图指标上均优于当前最先进方法。
原文摘要 · Abstract (English)
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.
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