MERSYS: A Collaborative Estimation and Dense Mapping System for Multi-Agent Generic SLAM

Multi-agent collaborative Simultaneous Localization and Mapping (SLAM) is an effective way for large-scale mapping. However, this approach, which relies on Visual-Inertial Odometry(VIO) as input, suffers from limitations such as susceptibility to environmental influences and the difficulty in accurately constructing dense 3D maps. To address these challenges, this paper presents Multi-Estimation Robust SLAM System (MERSYS), a novel framework for three-dimensional dense mapping based on the fusion of Lidar-Inertial Odometry(LIO) and VIO. Benefiting from lower communication’s costs and dense information acquisition capability, the proposed framework aims to achieve compatibility in processing both LIO and VIO inputs, establish joint loop closure detection to enable multi-map fusion, and then create a comprehensive global 3D dense point cloud map. Furthermore, an efficient communication strategy has been proposed to enable bidirectional transmission of dense and voluminous data. Experimental evaluations conducted on the publicly available HILTI SLAM 2021 dataset [10] as well as a real world dataset. Experimental results show that MERSYS achieves better results than state-of-the-art methods. The source code is available on the GitHub¹.