基于墙角族语义尺寸链的绑架定位研究

Research on Kidnapping Detection and Re-Localization Based on Semantic Dimensional Chain of Corner Family

针对目前原始自适应蒙特卡洛定位(Adaptive Monte Carlo Localization,AMCL)在相似环境下绑架检测容易出错且重定位极易失败等问题,提出基于墙角族语义尺寸链的改进AMCL算法.融合机器人多传感器信息和Gmapping算法构建二维栅格地图,基于Yolov5获取室内环境的目标检测框和类别信息,结合GrabCut算法和贝叶斯方法构建增量式语义映射地图;通过墙角的凸、凹和墙角相对于机器人的方位角对墙角进行分类,充分发掘语义映射地图中各墙角之间、墙角与室内物体之间的类别和位置关系,构建墙角族语义尺寸链和相应检索表;在定位过程中,基于墙角族语义尺寸链进行全局预定位,提出绑架检测机制进行绑架检测,在检测到绑架事件发生后,基于改进AMCL算法实现定位自恢复.最后,通过真实环境下的绑架实验验证了本文方法的有效性,实验表明,所提方法的全局定位准确率、全局定位速率、绑架检测准确率和绑架后定位准确率在相似环境下分别提升了42%、214%、88%和72%;在非相似环境下分别提升了44%、152%、12%和92%;在长走廊环境下分别提升了36%、426%、26%和68%.

In order to solve the problems of kidnapping detection and re-localization failure of original AMCL(Adap⁃tive Monte Carlo Localization)in similar environment,an improved AMCL algorithm based on semantic dimension chain of corner family is proposed.Firstly,the multi-sensor information of robot is fused and a two-dimensional grid map is con⁃structed based on Gmapping algorithm.Secondly,the target detection frame and category information of indoor environ⁃ment are obtained based on Yolov5,and the semantic mapping map is constructed incrementally by combining GrabCut al⁃gorithm and Bayesian method.The corners are classified based on their convexity,concavity,and the azimuth of the corners relative to the robot,and the category and position relationships between the corners and the indoor objects in the semantic mapping map are fully excavated.The semantic dimension chain of the corner family and the corresponding retrieval table are constructed.In the process of localization,global pre-localization is realized based on the semantic dimension chain of corner family,and kidnapping detection is carried out based on the proposed kidnapping detection mechanism,and localiza⁃tion self-recovery is realized based on the improved AMCL algorithm after the kidnapping event is detected.Finally,the ef⁃fectiveness of this method is verified by kidnapping experiments in real environment.Experiments show that the proposed method improves the global localization accuracy, global localization rate, kidnapping detection accuracy and localizationself-recovery success rate by 42%, 214%, 88% and 72%, respectively, in the similar environment;and 44%, 152%, 12% and92%, respectively, in the non-similar environment;and 36%, 426%, 26% and 68%, respectively, in the long corridor envi⁃ronment.