基于联合分布状态信息滤波的机器人协同定位

针对现有扩展卡尔曼滤波算法在协同定位应用计算复杂的问题,提出一种基于联合分布状态的信息滤波算法,并将其运用在多机器人协同定位中。从3个方面解决计算复杂的问题:第一,借鉴机器人同步构图与定位,利用联合分布状态将关键历史状态保留在滤波中,避免时间更新的复杂计算;第二,利用滤波信息参数的稀疏性,减小滤波所涉及的计算复杂度;第三,根据Cholesky矩阵分解的特殊性质,进一步减少计算复杂度,节省存储空间,简化通信管理,便于工作负载均衡分配。理论分析与仿真结果表明，该方法在确保计算与存储复杂度的同时保证了估计精度和协同定位的有效性。

Cooperative localization approach for robots based on joint distribution state-information filter

To solve the problem of computational complexity for the regular extended Kalman filter method in cooperative localization, an information filter based on joint distribution state to study cooperative localization for robots is presented. The proposed method consists of three key components to solve the computational complexity problem. Firstly, our approach preserves the historical states in the filter, which avoids the process of time updating that draws lessons from simultaneous localization and mapping. Secondly, the information parameters are sparse, thus the computational complexity of the filter is less. Finally, the special properties of the Cholesky modification algorithm are also used for further decreasing the computational complexity, which is convenient to distribute the work. The simulation result indicates that the method ensures these performance advantages as well as guarantees the estimation precision and the effectiveness of cooperative localization.