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.

翻译：在自主机器人领域，如何为主动协同SLAM（AC-SLAM）设计鲁棒的解决方案是一个重要挑战。该过程要求多个机器人通过智能协调其运动与传感器数据采集，协作探索未知环境并构建地图。本文提出一种利用空中与地面机器人进行环境探索与建图的高效视觉AC-SLAM方法。我们设计了一种高效边界滤波方法，该方法综合考虑公共IoU地图边界，并为每个机器人缩减待探索边界。此外，我们还提出一种引导机器人重返已访问目标位置的策略，以促进回环检测从而降低SLAM的不确定性。所提方法在ROS中实现，并通过公开数据集与同类方法的仿真实验进行评估，实现了累积覆盖率平均提升59%的效果。