计算机应用研究2017，Vol.34Issue(8)：2368-2371,4.DOI:10.3969/j.issn.1001-3695.2017.08.030
基于小生境遗传优化的Rao-Blackwellised SLAM算法
Rao-Blackwellised SLAM based on niched genetic optimized method
摘要
Abstract
Simulation localization and mapping (SLAM) is one of the key problems in realizing robot self-navigation.As an effective method for SLAM location, it widely used Rao-Blackwellised particle filter(RBPF) in the field of real time location.However, the RBPF behavior of frequent resampling results in particle impoverishment problem along with particles increased.In order to solve the problem and improve the algorithm performance, this paper proposed a RBPF SLAM algorithm based on improved niched genetic optimization (INGO-RBPF).The INGO-RBPF algorithm solves the robot path estimation using improved Rao-Blackwellised particle filter(PF), and solves the map estimation using extended Kalman filter (EKF).Finally the MATLAB simulations prove that INGO-RBPF performs well on estimated accuracy, stability, disturbance and location accuracy, and therefore it is suitable to apply in SLAM real-time location.关键词
同步定位与地图创建(SLAM)/Rao-Blackwellised粒子滤波器/小生境遗传算法/INGO-RBPFKey words
simulation location and mapping (SLAM)/Rao-Blackwellised particle filter/niched genetic algorithm/INGO-RBPF分类
信息技术与安全科学引用本文复制引用
陈建军,廖小飞,吴赟,陈光,庄新闯..基于小生境遗传优化的Rao-Blackwellised SLAM算法[J].计算机应用研究,2017,34(8):2368-2371,4.基金项目
国家自然科学基金资助项目(71171045) （71171045）
中央高校基本科研业务费专项基金资助项目(15D110422) （15D110422）
