  In autonomous robotics, a significant challenge involves devising robust
solutions for Active Collaborative SLAM (AC-SLAM). This process requires
multiple robots to cooperatively explore and map an unknown environment by
intelligently coordinating their movements and sensor data acquisition. In this
article, we present an efficient visual AC-SLAM method using aerial and ground
robots for environment exploration and mapping. We propose an efficient
frontiers filtering method that takes into account the common IoU map frontiers
and reduces the frontiers for each robot. Additionally, we also present an
approach to guide robots to previously visited goal positions to promote loop
closure to reduce SLAM uncertainty. The proposed method is implemented in ROS
and evaluated through simulations on publicly available datasets and similar
methods, achieving an accumulative average of 59% of increase in area coverage.
