室内非结构化环境三维栅格语义地图的构建  

Three-Dimensional Grid Semantic Map Building in Unstructured Indoor Environment

作　　者：吴皓[1] 田国会[1] 王家超[1] 周风余[1] 

机构地区：[1]山东大学控制科学与工程学院,济南250061

出　　处：《模式识别与人工智能》2012年第4期564-572,共9页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金(No.61075092);山东省自然科学基金(No.ZR2012FM031;ZR2011FM011)资助项目

摘　　要：针对室内复杂的非结构化环境和机器人动态变化的服务任务,提出基于快速识读码(QR code)技术的室内环境空间认知手段.在双目视觉获得深度信息的前提下,基于DSmT证据理论构建信息不确定数学模型,形成描述体素占有/空闲概率的三维栅格地图.在构建三维地图的同时,利用粘贴在大物品上的基于QR code技术的人工物标,为环境中的大物品添加语义标签,并基于大物品的尺寸更新对应的体素占空值,形成含大物品功能属性和归属关系描述的三维栅格语义地图.通过实验与其它信息融合算法进行对比,并对人工物标的识读准确性进行分析,证明该方法的有效性和可行性.Aiming at the complicated unstructured indoor environment and the dynamic service task of robots, an environment cognition method based on quick response （QR） code technology is proposed. Under the premise of gaining depth information with binocular vision, the uncertain mathematic model of binocular vision information is established on Dezert-Smarandache theory （DSmT） evidence theory. And the three-dimensional grid map is formed which describes the occupied/free probability of the voxel. While the three-dimensional map is structured, the semantic labels for the large objects are added by QR code based object mark plastered on large objects. The occupied/free values of the corresponding voxels are updated based on the dimension of the large object. Then, the three-dimensional grid semantic map is formed which includes the function property and the attributive relation of the large objects. The experiments are carried out to compare several information fusion algorithms with the proposed method and analyze the recognition accuracy for the artificial object marks. The results prove the availability and feasibility of the proposed method.

关 键 词：双目视觉 信息融合算法 体素 三维地图 快速识读码(QR code) 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]