SemanticSLAM: Using Environment Landmarks for Unsupervised Indoor Localization

Heba Abdelnasser, Reham Mohamed, Ahmed Elgohary, Moustafa Farid Alzantot, He Wang, Souvik Sen, Romit Roy Choudhury, Moustafa Youssef

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number7265092
Pages (from-to)1770-1782
Number of pages13
JournalIEEE Transactions on Mobile Computing
Volume15
Issue number7
DOIs
StatePublished - Jul 1 2016
Externally publishedYes

Keywords

  • Unconventional localization
  • indoor localization
  • semantic SLAM
  • unsupervised localization

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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