一种多无人机协同定位与稠密地图构建算法

针对VINS-Mono(monoculor visual-inertial state)算法定位精度较低且构建的稀疏点云地图包含的信息较少无法用于无人机的自主导航的问题，提出了一种集中式多无人机协同定位与稠密地图构建算法。该算法通过提高回环闭合中特征点匹配准确度以优化多无人机之间的定位精度，为了建立多个无人机之间的连接约束，使用四种不同类型的坐标系进行坐标转换优化，采用双向检测匹配法对关键帧进行特征点匹配，结合PROSAC(progressive sampling consensus)算法剔除误匹配，通过对有回环闭合约束的多无人机的位姿与全局地图进行全局位姿图优化，结合Voxblox构建了可供五架无人机协同导航的全局稠密地图。在EuRoc数据集的五个序列中，提出的多无人机协同定位与稠密地图构建算法与VINS-MONO算法相比，绝对轨迹误差分别降低了34%、26%、42%、24%、19%。实验证明，改进算法有效提高了多无人机之间的定位精度，并且构建的全局一致稠密地图包含距离信息与梯度信息，可以用于多无人机的自主导航。

A cooperative localization and dense map construction algorithm for multiple UAVs

Aiming at the problem that the VINS-MONO algorithm has low positioning accuracy and the sparse point cloud map constructed contains less information and cannot be used for autonomous navigation of UAVs, a centralized multi-UAV collaborative positioning and dense map construction algorithm is proposed. In order to establish the connection constraint between multiple UAVs, the algorithm uses four different types of coordinate systems for coordinate transformation optimization, and uses the two-way detection matching method to match the feature points of key frames, combined with the PROSAC (Progressive Sampling Consensus) algorithm to eliminate false matching. By optimizing the global pose map of multiple UAVs with loopback closure constraints, a global dense map for collaborative navigation of five UAVs was constructed by combining Voxblox. In the five sequences of the EuRoc dataset, the proposed multi-UAV co-localization and dense map construction algorithm reduces the absolute trajectory error by 34%, 26%, 32%, 24% and 19% compared with the VINS-MONO algorithm, respectively, and it is proved by experiments that the improved algorithm effectively improves the positioning accuracy between multiple UAVs, and the constructed globally consistent dense map contains distance information and gradient information, which can be used for autonomous navigation of multiple UAVs.