摘要
为解决快速同步定位与地图构建算法因粒子退化导致SLAM(simultaneous location and mapping)估计精度不佳的问题,提出一种融合渐消自适应无迹粒子滤波与高斯分布重采样的FastSLAM算法。通过融合渐消滤波和无迹粒子滤波,产生一种自适应提议分布,利用高斯分布对高权重粒子进行分散得到新粒子。建立机器人运动模型和观测模型,并在仿真环境中进行性能验证。仿真结果表明:该算法能有效地缓解粒子退化,增加系统稳定性,提高SLAM估计精度。
To solve the low estimating accuracy of SLAM(simultaneous location and mapping)caused by particle degradation in fast simultaneous location and mapping algorithm,a FastSLAM algorithm which combine fading adaptive unscented particle filtering and Gaussian distributed re-sampling is proposed.An adaptive proposal distribution was generated by combining fading filtering and unscented particle filtering,and the high-weight particles were dispersed by Gaussian distribution to get new particles.The motion model and observation model of robot were established,and the performance was tested in simulation environment.The simulation result shows that,the algorithm can effectively alleviate particle degradation,increase system stability and improve SLAM estimation accuracy.
作者
朱友帅
袁明新
姜烽
张全兵
Zhu Youshuai;Yuan Mingxin;Jiang Feng;Zhang Quanbing(School of Mechanical Engineering,Jiangsu University of Science&Technology,Zhenjiang 212003,China;Zhangjiagang Industrial Technology Research Institute,Jiangsu University of Science&Technology,Zhangjiagang 215600,China)
出处
《兵工自动化》
2020年第2期87-92,共6页
Ordnance Industry Automation
基金
国家自然科学基金(61105071)
江苏科技大学张家港校区研究生创新工程(128180206)
关键词
同步定位与地图构建
粒子退化
自适应提议分布
高斯分布重采样
simultaneous location and map establishment
particle degradation
adaptive proposal distribution
Gaussian distribution re-sampling
作者简介
朱友帅(1992-),男,江苏人,硕士,从事移动机器人自主导航技术研究。E-mail:zysforwork@163.com。
