哈尔滨工程大学学报2025，Vol.46Issue(2)：363-373,11.DOI:10.11990/jheu.202210044
以多数语义物体为主特征的语义地图重定位研究
Semantic map relocalization using the primary features of most semantic objects
摘要
Abstract
A semantic map relocalization algorithm with the majority of semantic objects as primary features for glob-al localization is proposed to address the localization inaccuracies of the adaptive Monte Carlo localization(AMCL)algorithm in similar environments,long corridors,and after environmental changes.The algorithm initially extracts the primary features of semantic objects from a preconstructed 2D grid semantic map,then combines these features with the camera observation model and information table about the primary semantic objects and surrounding sec-ondary semantic objects to achieve global prelocalization.Then,the particle weight update method is improved on the basis of the prelocalization results,ultimately enhancing the real-time performance of the AMCL algorithm.The results show that,compared with the AMCL algorithm,the proposed algorithm has improved the localization rate by 68.75%in similar indoor environments and by 52.78%after environmental change.In long corridor environments,after environmental changes,the localization rate has improved by 65.96%and 53.13%compared with that using the AMCL algorithm.Experiments confirm that the proposed algorithm can improve particle convergence rate,ro-bustness,and real-time performance.关键词
语义地图/主特征/相机/信息表/全局预定位/粒子/自适应蒙特卡罗定位算法/定位速率Key words
semantic map/main feature/camera/information table/global prepositioning/particle/adaptive Monte Carlo localization(AMCL)/positioning rate分类
计算机与自动化引用本文复制引用
蒋林,明祥宇,汤勃,万乐,向贤宝,雷斌,郭宇飞..以多数语义物体为主特征的语义地图重定位研究[J].哈尔滨工程大学学报,2025,46(2):363-373,11.基金项目
国家自然科学基金项目(51874217) （51874217）
国家重点研发计划(2019YFB1310000) （2019YFB1310000）
湖北省重点研发计划(2020BAB098). （2020BAB098）
