Map building for dynamic environments using grid vectors  

作　　者：Wen-fei WANG,Rong XIONG ,Jian CHU (State Key Laboratory of Industrial Control Technology,Institute of Cyber-Systems and Control,Zhejiang University,Hangzhou 310027,China) 

出　　处：《Journal of Zhejiang University-Science C(Computers and Electronics)》2011年第7期574-588,共15页浙江大学学报C辑（计算机与电子（英文版）

基　　金：supported by the National Natural Science Foundation of China (Nos.60675049 and 61075078);the National High-Tech Research and Development Program (863) of China (No. 2008AA04Z209)

摘　　要：This paper addresses the problem of creating a geometric map with a mobile robot in a dynamic indoor environment.To form an accurate model of the environment,we present a novel map representation called the 'grid vector',which combines each vector that represents a directed line segment with a slender occupancy grid map.A modified expectation maximization (EM) based approach is proposed to evaluate the dynamic objects and simultaneously estimate the robot path and the map of the environment.The probability of each grid vector is evaluated in the expectation step and then used to distinguish the vector into static and dynamic ones.The robot path and map are estimated in the maximization step with a graph-based simultaneous localization and mapping (SLAM) method.The representation we introduce provides advantages on making the SLAM method strictly statistic,reducing memory cost,identifying the dynamic objects,and improving the accuracy of the data associations.The SLAM algorithm we present is efficient in computation and convergence.Experiments on three different kinds of data sets show that our representation and algorithm can generate an accurate static map in a dynamic indoor environment.This paper addresses the problem of creating a geometric map with a mobile robot in a dynamic indoor environment.To form an accurate model of the environment,we present a novel map representation called the ＇grid vector＇,which combines each vector that represents a directed line segment with a slender occupancy grid map.A modified expectation maximization （EM） based approach is proposed to evaluate the dynamic objects and simultaneously estimate the robot path and the map of the environment.The probability of each grid vector is evaluated in the expectation step and then used to distinguish the vector into static and dynamic ones.The robot path and map are estimated in the maximization step with a graph-based simultaneous localization and mapping （SLAM） method.The representation we introduce provides advantages on making the SLAM method strictly statistic,reducing memory cost,identifying the dynamic objects,and improving the accuracy of the data associations.The SLAM algorithm we present is efficient in computation and convergence.Experiments on three different kinds of data sets show that our representation and algorithm can generate an accurate static map in a dynamic indoor environment.

关 键 词：Grid vector LINE SEGMENTS Dynamic Simultaneous localization and mapping (SLAM) Expectation maximization (EM) 

分 类 号：P283[天文地球—地图制图学与地理信息工程]