空间语义地图机器人自主在线构建方法研究

项目名称： 空间语义地图机器人自主在线构建方法研究

项目编号： No.61305103

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 王珂

作者单位： 哈尔滨工业大学

项目金额： 24万元

中文摘要： 空间语义地图在线自主构建是移动机器人研究的新热点。本课题旨在突破既有研究的局限,面向物体及地点两种语义类别,建立空间语义地图的机器人自主在线构建的新方法。课题形成三个功能明确,兼容良好的研究内容:①面向语义环境特征的空间地图在线构建,研究基于Kinect物体复合特征提取,改进SRUKF局部SLAM,基于全景视觉的地图在线谱分割技术,解决底层SLAM效能和与识别系统的协作问题；②语义环境特征的机器人在线学习及识别,研究Web数据库模型库构建方法,SVMs在线增量训练方法以及地点识别方法,解决分类器在线学习适应性问题；③空间语义地图在线量化更新机制,提出概率量化表述及跨层次概率传播方法,提出概率多假设锚固PMHA解决语义地图动态量化更新问题。课题采用全景摄像机及Kineect传感器,以PeopleBot机器人验证相关工作。研究成果将极大提高机器人高级环境认知水平，为其实际应用奠定理论基础。

中文关键词： 空间语义地图；同时地图构建及定位；物体提取及跟踪；谱分割；概率更新机制

英文摘要： Online autonomous building of spatial semantic map is currently a hot topic of mobie robot research. This project is designed to break through the limitations of existing research, and towards two semantic categories, namely object and place, to estabilish a novel method of online autonomous building of spatial semantic map for mobile robot. The topics forms three explicit and compatible contents: ①Semantic enviromental feature oriented online spatial map buidling, which includes objects composite feature extraction by using Kinect, improved SRUKF local SLAM method, and online spectrum partition of navigation map base on panoramic vision, will solve the efficiency issue of underlying SLAM and coordination between recognition system; ②Online learning and recognition of spatial environmental features for robot,which covers the construction method of Web-based object database, SVMs online incremental training and place recognition, will impove the adaptive ability of robot classifier; ③Quantization updating scheme of spatial semantic map, which contains map probabilistic representation and its cross-level propagation, proposing a probabilistic multiple hypothesis anchoring method for dynamic updating semantic map. The projects adopts Kinect and Omni-directioal vision as the main external sensors, and PeopleBot robo

英文关键词： space semantic map；slam；object extraction and tracking；spectral partition；probabilistic update scheme