Coxgraph: Multi-Robot Collaborative, Globally Consistent, Online Dense Reconstruction System

• Published in: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 8722 - 8728
• Main Authors: Liu, Xiangyu, Ye, Weicai, Tian, Chaoran, Cui, Zhaopeng, Bao, Hujun, Zhang, Guofeng
• Format: Conference Proceeding
• Language: English
• Published: IEEE 27-09-2021
• Subjects: Collaboration; Multi-robot systems; Real-time systems; Three-dimensional displays; Time factors; Trajectory; Transforms
• ISSN: 2153-0866
• DOI: 10.1109/IROS51168.2021.9636645

Real-time dense reconstruction has been extensively studied for its wide applications in computer vision and robotics, meanwhile much effort has been made for the multi-robot system which plays an irreplaceable role in complicated but time-critical scenarios, e.g., search and rescue tasks. In this paper, we propose an efficient system named Coxgraph for multi-robot collaborative dense reconstruction in real-time. In our system, each client performs volumetric mapping in a producer-consumer manner. To facilitate transmission, we propose a compact 3D representation which transforms the SDF submap to mesh packs. During the recovery of submaps from mesh packs, the system can perform loop closure outlier rejection based on geometry consistency, trajectory collision and fitness check. Then we develop a robust map fusion method through joint optimization of trajectories and submaps. Extensive experiments demonstrate that our system can produce a globally consistent dense map in real-time with less transmission load, which is available as open-source software 1 .