摘要
针对移动机器人全局重定位时易出现定位错误的问题,本文提出一种基于构建的语义地图,并加载语义似然估计的粒子滤波重定位的解决方法.利用激光雷达建立环境栅格地图,同时结合三维深度相机对物体的识别与定位信息,赋予栅格语义信息,得到环境语义地图.在重定位过程中,通过粒子滤波方法同时进行栅格地图结构匹配与环境语义信息的匹配,以推算机器人在地图上的实际位置.通过实验证明所提出方法克服了现有粒子滤波方法仅利用环境结构信息进行匹配的不足,有效解决机器人全局重定位容易出错的问题,增强了重定位的鲁棒性,同时增强了重定位的收敛速度.
Aiming at the problem that mobile robots are prone to localization errors during global relocation,this paper proposes a particle filter relocation method based on the constructed semantic map and loading semantic likelihood estimation to solve the problem.Using the lidar to establish the environmental grid map,meanwhile combing with the three-dimensional depth camera’s object recognition and positioning information,the environmental semantic map is obtained by giving semantic information.During the relocation process,the particle filter method is used to simultaneously match the grid map structure and the semantic information of the environment to calculate the actual position of the robot on the map,and accurately to realize the position relocation.Experiments results show that this method can overcome the shortcomings of the original particle filtering method that only uses environmental structure information for matching,also solve the problem of robot global relocation error-prone and enhance the robustness of relocation,and enhances the convergence speed of relocation.
作者
蒋林
向超
朱建阳
刘奇
JIANG Lin;XIANG Chao;ZHU Jian-yang;LIU Qi(Key Laboratory of Metallurgical Equipment and Control Technology,Ministry of Education,Wuhan University of Science and Technology,Wuhan,Hubei 430081,China;Institute of Robotics and Intelligent Systems,Wuhan University of Science and Technology,Wuhan,Hubei 430081,China)
出处
《电子学报》
EI
CAS
CSCD
北大核心
2021年第2期306-314,共9页
Acta Electronica Sinica
基金
国家重点研发计划项目(No.2019YFB1310000)
湖北省自然科学基金(No.2018CFB626)
武汉市应用基础前沿项目(No.2019010701011404)
机器人与智能系统研究院开放基金(No.F201803)。
关键词
全局重定位
语义地图
粒子滤波
语义似然
global location
semantic map
particle filter algorithm
semantic likelihoo
