广域动态的野外环境中移动机器人六维全局定位方法的研究

项目名称： 广域动态的野外环境中移动机器人六维全局定位方法的研究

项目编号： No.61503245

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 自动化技术、计算机技术

项目作者： 何弢

作者单位： 上海交通大学

项目金额： 17万元

中文摘要： 定位问题是移动机器人研究领域中一个基础且重要的问题。近年来，移动机器人的应用逐步拓展到了很多广域动态的野外环境中，野外环境下的移动机器人定位问题凸现其研究价值。本课题将针对广域动态的野外环境，研究观测驱动型全局定位方法中的地图匹配问题和数据融合问题，进而提出一种新的观测驱动型全局定位系统，实现在广域动态的野外环境中移动机器人六维全局定位。课题研究在地形起伏的野外环境中三维环境信息的表征、提取和组织方法；研究在存在动态干扰物和观测噪声的前提下，高效鲁棒的三维地图匹配技术；进而研究基于观测驱动型贝叶斯滤波器的数据融合方法，其将有效处理野外环境中的各种不确定性，并在定位失败的前提下快速重新定位。课题将在真实的广域动态野外环境中开展全局定位的实验。本课题研究对移动机器人野外环境中的定位理论的发展以及拓展移动机器人在野外环境中的应用具有积极意义。

中文关键词： 全局定位；特征匹配；数据融合

英文摘要： Global localization has long been considered as one of the most important but also challenging problems for mobile robots operating in the large-scale and dynamic field environment. Current studies of global localization in literature are mainly based on the Bayesian filtering technique which can provide an elegant statistical framework for the uncertainty management and multisensory fusion. However the majority of implementations of Bayesian-filters for global localization obey the same update rules in such a location driven sense that they guess the robot location first and then adjust the guess by incorporating the current observation data. This leads to some problematic consequence that the system suffers from great computational load in large application area and it cannot recover from localization failure. Thus the majority of conventional global localization systems cannot be applied in the large-scale and dynamic field environment. Thus this project breaks out the conventional update rules of Bayes filters and proposes a new approach – the observation driven Bayes filters (OD-BF). As the name implies, OD-BFs estimate the robot state just according to the most recent observations and then adjust the estimate by incorporating the dead reckoning information. We further implement an observation driven Bayes filter for globally estimating the robot pose in the large-scale and dynamic field environment. This global localization system features in an effective feature matching framework which has a high robustness against uncertainties from sensor occlusions and extraneous observation in a highly dynamic field environment. Additionally our project implements a data fusion system which is able to deal with global initialization and have the ability of quick recovery from a localization failure. We will carry out sufficient practical experiments to determine both advantages and disadvantages of our proposed global localization system.

英文关键词： global localization;feature matching;data fusion