Joint simultaneous localization and mapping (SLAM) constitutes the basis for cooperative action in multi‐robot teams. We designed a stereo vision‐based 6D SLAM system combining local and global methods to benefit from their particular advantages: (1) Decoupled local reference filters on each robot for real‐time, long‐term stable state estimation required for stabilization, control and fast obstacle avoidance; (2) Online graph optimization with a novel graph topology and intra‐ as well as inter‐robot loop closures through an improved submap matching method to provide global multi‐robot pose and map estimates; (3) Distribution of the processing of high‐frequency and high‐bandwidth measurements enabling the exchange of aggregated and thus compacted map data. As a result, we gain robustness with respect to communication losses between robots. We evaluated our improved map matcher on simulated and real‐world datasets and present our full system in five real‐world multi‐robot experiments in areas of up 3,000 m2 (bounding box), including visual robot detections and submap matches as loop‐closure constraints. Further, we demonstrate its application to autonomous multi‐robot exploration in a challenging rough‐terrain environment at a Moon‐analogue site located on a volcano.