Simultaneous Localization and Map Building Using Constrained State Estimate Algorithm
会议名称：《第二十七届中国控制会议》
会议日期：2008年
学科分类：080202[工学-机械电子工程] 082304[工学-载运工具运用工程] 08[工学] 0804[工学-仪器科学与技术] 080204[工学-车辆工程] 0802[工学-机械工程] 0823[工学-交通运输工程]
关 键 词：SLAM Outdoors navigation Guidance Mobile vehicles State Constraints Estimation
摘 要：Intelligent vehicles and autonomous robots are viable in complex environments,the reliable and robust localization function of which is *** to the large variability and uncertainty of complex environments,it is difficult to rely on a specific method or a set of sensor data to correctly and robustly estimate the robot *** key to solving the localization problem is to optimally use and fuse all useful sources of information available to the mobile *** is common to have approximate digital maps of the road network.A framework for simultaneous localization and map building（SLAM） problems using road constrained Kalman filter algorithms is developed,with the emphasis on vehicle applications in large *** presents the problems of outdoor navigation in areas with combination of features and onroad *** aided SLAM algorithms,which incorporate absolute information in a consistent manner,are *** filters are commonly used to estimate the states of a mobile ***,in the application of Kalman filters,the known model or signal information often are either ignored or dealt with *** instance,constraints on state values which may be based on physical considerations are often neglected because they do not fit easily into the structure of the Kalman *** paper develops a rigorous analytic method of incorporating state equality constraints in the Kalman *** constraints may be time-varying,but it significantly improves the prediction accuracy of the *** SLAM implementation uses the road constrained kalman filter algorithm to maintain the error of vehi-cle’s location and ***,the use of this algorithm is demonstrated on a simple vehicle tracking problem.