东南大学学报（自然科学版）2009，Vol.39Issue(5)：923-927,5.DOI:10.3969/j.issn.1001-0505.2009.05.011
基于混合模型的移动机器人同时定位与环境建模
Mixed-model based simultaneous localization and mapping approach for mobile robot
摘要
Abstract
A new simultaneous localization and mapping (SLAM) approach based on a mixed map model using sonar data and odometry information is presented. The mixed model composed of occupancy grids and line maps is utilized to represent the environment map. Firstly, three region models and Bayes' rules are used to construct an occupancy grid map. The map precision is enhanced through fusing the information of several sonar sensors at different times. Then, the Hough transform is introduced to extract line features and the line feature maps are created. The local map and the global map are matched by comparing orientation, collinearity and overlap of the straight-line segment in the maps. Finally, the simultaneous localization and mapping are accomplished with the line features and extended Kalman filter through state prediction, observation prediction and estimation phase, which can estimate the robot pose and correct the map model. The simulation results and the real experimental results indicate the feasibility and validity of this approach.关键词
移动机器人/同时定位与环境建模/贝叶斯法则/扩展卡尔曼滤波器Key words
mobile robot/ simultaneous localization and mapping/ Bayes' rules/ extended Kalman filter分类
信息技术与安全科学引用本文复制引用
房芳,马旭东,戴先中..基于混合模型的移动机器人同时定位与环境建模[J].东南大学学报（自然科学版）,2009,39(5):923-927,5.基金项目
国家自然科学基金资助项目(60805032)、国家高技术研究发展计划(863计划) 资助项目 (2007AA041703, 2006AA040202). （60805032）
