A Hybrid Indoor Positioning System Using a Linear Weighted Policy Learner and Iterative PDR

计算机科学 障碍物 卡尔曼滤波器 定位系统 构造(python库) 航位推算 计算机视觉 实时计算 人工智能 全球定位系统 电信 几何学 点(几何) 数学 程序设计语言 政治学 法学
作者
Walter Charles Sousa Seiffert Simões,Walmir Acioli E Silva,Mateus Martínez de Lucena,Nasser Jazdi,Vicente Ferreira de Lucena
出处
期刊:IEEE Access [Institute of Electrical and Electronics Engineers]
卷期号:8: 43630-43656 被引量:7
标识
DOI:10.1109/access.2020.2977501
摘要

Electronic indoor positioning systems deal with the combination of sensors, actuators, and computational algorithms for precisely locating subjects, delivering navigation directives, and keeping track of particular objects. The main factors considered for the construction and evaluation of these systems are the localization accuracy and the time spent to calculate and deliver this information. The challenge in developing successful positioning systems is to find a tolerable relationship between those factors. In this proposal, after a careful analyses of related works, we associated different methodologies and technologies to construct a hybrid positioning model that uses a mapping algorithm called Linear Weighted Policy Learner, a navigation model called iterative Pedestrian Dead Reckoning (which uses the Kalman filter to deliver real-time location), and an obstacle detection algorithm that combines sounds and stereo vision sensorial capabilities. The adopted choices were based on the published state-of-the-art, and comparisons of the obtained results showed that our system is accurate and fast enough to be very competitive with the current stage of the technology.
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