There is an extensive literature regarding multi-robot simultaneous localization and mapping (MRSLAM). In most part of the research, the environment is assumed to be static, while the dynamic parts of the environment degrade the estimation quality of SLAM algorithms and lead to inherently fragile systems. To enhance the performance and robustness of the SLAM in dynamic environments (SLAMIDE), a novel cooperative approach named parallel-map (p-map) SLAM is introduced in this paper. The objective of the proposed method is to deal with the dynamics of the environment, by detecting dynamic parts and preventing the inclusion of them in SLAM estimations. In this approach, each robot builds a limited map in its own vicinity, while the global map is built through a hybrid centralized MRSLAM. The restricted size of the local maps, bounds computational complexity and resources needed to handle a large scale dynamic environment. Using a probabilistic index, the proposed method differentiates between stationary and moving landmarks, based on their relative positions with other parts of the environment. Stationary landmarks are then used to refine a consistent map. The proposed method is evaluated with different levels of dynamism and for each level, the performance is measured in terms of accuracy, robustness, and hardware resources needed to be implemented. The method is also evaluated with a publicly available real-world data-set. Experimental validation along with simulations indicate that the proposed method is able to perform consistent SLAM in a dynamic environment, suggesting its feasibility for MRSLAM applications.

有大量关于多机器人同时定位和映射 (MRSLAM) 的文献。在大部分研究中，假设环境是静态的，而环境的动态部分会降低 SLAM 算法的估计质量并导致系统固有的脆弱性。为了提高动态环境（SLAMIDE）中SLAM的性能和鲁棒性，本文介绍了一种称为并行映射（p-map）SLAM的新型协作方法。所提出方法的目标是通过检测动态部分并防止将它们包含在 SLAM 估计中来处理环境的动态。在这种方法中，每个机器人在其自身附近构建一个有限的地图，而全局地图则通过混合集中式 MRSLAM 构建。本地地图的大小限制，限制处理大规模动态环境所需的计算复杂性和资源。使用概率指数，所提出的方法根据它们与环境其他部分的相对位置来区分静止和移动的地标。然后使用固定地标来改进一致的地图。对所提出的方法进行了不同级别的动态评估，并且对于每个级别，根据准确性、鲁棒性和需要实现的硬件资源来衡量性能。该方法还使用公开可用的真实世界数据集进行评估。实验验证和模拟表明，所提出的方法能够在动态环境中执行一致的 SLAM，表明其在 MRSLAM 应用中的可行性。