计算机科学
航位推算
加速度计
同时定位和映射
实时计算
人工智能
概率逻辑
计算机视觉
惯性测量装置
惯性导航系统
Android(操作系统)
移动机器人
全球定位系统
机器人
方向(向量空间)
电信
数学
操作系统
几何学
作者
Heba Abdelnasser,Reham Hussein Mohamed,Ahmed Elgohary,Moustafa Alzantot,He Wang,Souvik Sen,Romit Roy Choudhury,Moustafa Youssef
标识
DOI:10.1109/tmc.2015.2478451
摘要
Indoor localization using mobile sensors has gained momentum lately. Most of the current systems rely on an extensive calibration step to achieve high accuracy. We propose SemanticSLAM, a novel unsupervised indoor localization scheme that bypasses the need for war-driving. SemanticSLAM leverages the idea that certain locations in an indoor environment have a unique signature on one or more phone sensors. Climbing stairs, for example, has a distinct pattern on the phone's accelerometer; a specific spot may experience an unusual magnetic interference while another may have a unique set of Wi-Fi access points covering it. SemanticSLAM uses these unique points in the environment as landmarks and combines them with dead-reckoning in a new Simultaneous Localization And Mapping (SLAM) framework to reduce both the localization error and convergence time. In particular, the phone inertial sensors are used to keep track of the user's path, while the observed landmarks are used to compensate for the accumulation of error in a unified probabilistic framework. Evaluation in two testbeds on Android phones shows that the system can achieve 0.53 meters human median localization errors. In addition, the system can detect the location of landmarks with 0.83 meters median error. This is 62 percent better than a system that does not use SLAM. Moreover, SemanticSLAM has a 33 percent lower convergence time compared to the same systems. This highlights the promise of SemanticSLAM as an unconventional approach for indoor localization.
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