基于改进AMCL与点云匹配校正的落布机器人定位分析
Positioning Method of Cloth Roller Conveying Robot Based on Improved Amcl and Point Cloud Matching Correction
游刚 1李世芸 1仇隽挺 2周圣云 3张博文1
作者信息
- 1. 昆明理工大学 机电工程学院,昆明 650500
- 2. 浙江工业大学 机械工程学院,杭州 310014
- 3. 浙江万兔思睿机器人有限公司,浙江温州 325105
- 折叠
摘要
为了解决落布机器人在纺织车间应用时,由于计算效率低和粒子贫化导致的定位精度降低问题,本文提出了一种基于AMCL(Adaptive Monte Carlo Localization)与点云匹配校正的全局定位方法.首先由AMCL中KLD(Kullback Leibler distance)采样动态删除冗余粒子,并利用蝙蝠算法优化KLD调整后的粒子集,提高粒子多样性,有效压缩粒子规模,从而实现计算精度和效率的双重提升,最后通过NDT(Normal Distribution Trans-form)算法对二维栅格地图进行高精度激光测量匹配,对AMCL的全局位姿进一步修正,提高定位精度.实验结果验证了本文算法的有效性与可行性.
Abstract
In order to address the issue of reduced positioning accuracy in textile workshops when using cloth laying robots,this paper proposes a global positioning method based on adaptive Monte Carlo localization(AMCL)and point cloud matching correction.The proposed method involves dynamically deleting redundant particles in AMCL using Kullback Leibler distance(KLD)sampling,and optimizing the particle set adjusted by KLD using the bat algorithm to improve particle diversity and compress the particle scale.This leads to improvement in both computational accuracy and efficiency.Furthermore,the global pose of AMCL is corrected by high-precision laser measurement matching of the two-dimensional grid map using the normal distribution transform(NDT)algorithm,which enhances positioning accuracy.The effectiveness and feasibility of the proposed algorithm are validated through the experimental results.
关键词
AMCL/NDT/蝙蝠算法/粒子贫化/全局定位Key words
AMCL/NDT/bat algorithm/particle depletion/global localization引用本文复制引用
出版年
2024
NETL
NSTL
万方数据