摘要
为了满足煤矿巷道喷浆机器人自主移动对精确环境地图的需求,对Gmapping算法在大场景中易发生权值退化和粒子贫化,导致机器人位姿估计误差过大以及地图重叠、分层等一致性变差的问题,提出分类回收重采样算法.在重采样过程中,按比例将低权重粒子修正回收,充分利用现有信息,在抑制权值退化的同时尽量保护粒子多样性.试验结果表明,在对ACES building和MIT Killian Court数据集进行建图时,对利用传统算法定位和建图效果很差的粒子数,改进后的Gmapping算法仍能将机器人平移误差和旋转误差维持在较低的水平,并能获得清晰、准确的环境地图;采用分类回收重采样算法后期粒子分布情况更加符合粒子滤波要求,验证了分类回收重采样算法的有效性.
The autonomous movement of shotcrete robot in coal mine roadways requires an accurate environment map,and the weight degradation and particle dilution in large scenes are easy to happen in the Gmapping algorithm,which leads to excessive pose estimation error of robot and poor consistency such as map overlap and layering.Therefore a classification recovery resampling algorithm is proposed.When resampling,it modifies and recovers the low-weight particles in proportion,which makes full use of the existing information and tries to protect the particle diversity while restraining the weight degradation.The experimental results show that when mapping ACES building and MIT Killian Court data sets,using the traditional algorithm to locate and map the number of particles with extremely poor effect,the improved Gmapping algorithm can still maintain the translation error and rotation error of the robot at a low level,and can obtain a clear and accurate environment map.The particle distribution in the later stage of the classification recovery resampling algorithm is more in line with the requirements of the particle filter,which verifies the effectiveness of the classification recovery resampling algorithm.
作者
韩彦峰
李君君
肖科
HAN Yanfeng;LI Junjun;XIAO Ke(College of Mechanical and Vehicle Engineering,Chongqing University,Chongqing 400030,China)
出处
《湖南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2023年第6期118-126,共9页
Journal of Hunan University:Natural Sciences
基金
国家重点研发计划资助项目(2018YFB1304800)。
关键词
巷道
地图构建
分类回收重采样算法
tunnels
mapping
classification recovery resampling algorithm
