CAJ | 학술논문

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。
침대현유확전잡이만려파산법재협동정위응용계산복잡적문제，제출일충기우연합분포상태적신식려파산법，병장기운용재다궤기인협동정위중。종3개방면해결계산복잡적문제：제일，차감궤기인동보구도여정위，이용연합분포상태장관건역사상태보류재려파중，피면시간경신적복잡계산；제이，이용려파신식삼수적희소성，감소려파소섭급적계산복잡도；제삼，근거 Cholesky 구진분해적특수성질，진일보감소계산복잡도，절성존저공간，간화통신관리，편우공작부재균형분배。이론분석여방진결과표명，해방법재학보계산여존저복잡도적동시보증료고계정도화협동정위적유효성。
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 com-plexity 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 param-eters 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.