We present a real-time monocular simultaneous localization and mapping (SLAM) system with a new distributed structure for multi-UAV collaboration tasks. The system is different from other general SLAM systems in two aspects: First, it does not aim to build a global map, but to estimate the latest relative position between nearby vehicles; Second, there is no centralized structure in the proposed system, and each vehicle owns an individual metric map and an ego-motion estimator to obtain the relative position between its own map and the neighboring vehicles’. To realize the above characteristics in real time, we demonstrate an innovative feature description and matching algorithm to avoid catastrophic expansion of feature point matching workload due to the increased number of UAVs. Based on the hash and principal component analysis, the matching time complexity of this algorithm can be reduced from O(log N) to O(1). To evaluate the performance, the algorithm is verified on the acknowledged multi-view stereo benchmark dataset, and excellent results are obtained. Finally, through the simulation and real flight experiments, this improved SLAM system with the proposed algorithm is validated.

我们提出了具有单分布式实时结构的实时单眼同时定位和制图（SLAM）系统，用于多无人机协作任务。该系统在两个方面与其他通用SLAM系统不同：首先，它的目的不是建立全局地图，而是估计附近车辆之间的最新相对位置。其次，在所提出的系统中没有集中式结构，并且每辆车拥有一个单独的度量图和一个自我运动估计器，以获取其自己的图与相邻车之间的相对位置。为了实时实现上述特征，我们演示了一种创新的特征描述和匹配算法，以避免由于无人机数量增加而导致的特征点匹配工作量的灾难性扩展。根据哈希和主成分分析，O（log N）至O（1）。为了评估性能，该算法在公认的多视图立体基准数据集上进行了验证，并获得了出色的结果。最后，通过仿真和实际飞行实验，验证了该改进的SLAM系统的有效性。