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首页> 外文期刊>IEEE Transactions on Industrial Electronics >Multisensor Fusion-Based Concurrent Environment Mapping and Moving Object Detection for Intelligent Service Robotics
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Multisensor Fusion-Based Concurrent Environment Mapping and Moving Object Detection for Intelligent Service Robotics

机译：基于多传感器融合的并发环境映射和智能服务机器人运动对象检测

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摘要

Intelligent service robot development is an important and critical issue for human community applications. With the diverse and complex service needs, the perception and navigation are essential subjects. This investigation focuses on the synergistic fusion of multiple sensors for an intelligent service robot that not only performs self-localization and mapping but also detects moving objects or people in the building it services. First of all, a new augmented approach of graph-based optimal estimation was derived for concurrent robot postures and moving object trajectory estimate. Moreover, all the moving object detection issues of a robot's indoor navigation are divided and conquered via multisensor fusion methodologies. From bottom to up, the estimation fusion methods are tactically utilized to get a more precise result than the one from only the laser ranger or stereo vision. Furthermore, for solving the consistent association problem of moving objects, a covariance area intersection belief assignment is applied for motion state evaluation and the complementary evidences such as kinematics and vision features are both synergized together to enhance the association efficiency with the evidence fusion method. The proof of concept with experiments has been successfully demonstrated and analyzed.
机译：智能服务机器人的开发对于人类社区应用而言是一个重要且至关重要的问题。随着多样化和复杂的服务需求，感知和导航是必不可少的主题。这项研究集中于智能服务机器人的多个传感器的协同融合，该机器人不仅执行自我定位和地图绘制，而且还可以检测其服务的建筑物中的移动物体或人。首先，针对并行机器人姿态和运动对象轨迹估计，推导了一种新的基于图的最佳估计的增强方法。此外，通过多传感器融合方法对机器人室内导航的所有运动对象检测问题进行了划分和征服。从底到上，从战术上讲，估计融合方法被利用来获得比仅来自激光测距仪或立体视觉的方法更精确的结果。此外，为了解决运动对象的一致性关联问题，将协方差区域相交置信分配用于运动状态评估，并通过证据融合方法将运动学和视觉特征等互补证据协同增效，以提高关联效率。实验的概念证明已被成功证明和分析。

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