Vision multi-robot cooperative SLAM mainly uses cameras as sensors and achieves localization and map building through multi-robot cooperation. However, the front-end computation is too large in the face of complex environments, which tends to lead to unsatisfactory overall system accuracy. Inspired by the lightweight features of REVO and SVO algorithms, this article proposes a multirobot cooperative SLAM system based on lightweight intelligence, aiming to reduce front-end computational resources while improving system scalability. This article proposes an improved REVO algorithm-L-REVO to realize the front-end real-time operation through lightweight improvement; fusing L-REVO with the back-end of CCMSLAM system to propose a complete multi-machine collaborative SLAM architecture; adjusting the front-end sensors and algorithms to verify the impact on system performance when the front-end is homogeneous or heterogeneous, respectively. On the public dataset TUM, the system improves the localization accuracy by 59. 4% and 31. 6% in both modes, respectively, and the energy efficiency ratio by 8 times compared with the CCMSLAM system. Finally, the system is used for indoor scenario experiments with a front-end power consumption of only 1. 43 W, which verifies the feasibility and effectiveness of the proposed system.