A Bayesian occupancy grid filter for robust pedestrian dead reckoning
Bhandary Karnoor, Sahil
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Permalink
https://hdl.handle.net/2142/122157
Description
Title
A Bayesian occupancy grid filter for robust pedestrian dead reckoning
Author(s)
Bhandary Karnoor, Sahil
Issue Date
2023-12-07
Director of Research (if dissertation) or Advisor (if thesis)
Roy Choudhury, Romit
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Indoor Navigation, Pedestrian Dead Reckoning, Inertial Measurement Units, Bayesian Filtering
Abstract
This thesis considers the problem of indoor localization using inertial sensors (IMUs) that are embedded in almost all smartphones and mobile devices. Significant research has focused on developing pedestrian dead reckoning (PDR) algorithms that utilize the IMU measurements to track human movement. While Particle Filters (PF) have offered the best-known accuracy so far, they are also known to suffer from low robustness. This is not surprising given how IMU data from the real world is highly noisy, mainly due to the arm and limb gestures of the user.
Since real-world deployments often favor robustness over accuracy, I propose a Bayesian Occupancy Grid Filter (BOF) that can absorb far greater IMU error compared to PFs. The robustness gains are shown through extensive simulations and the implementation of a fully functional real-time system that performs in accordance with our expectations. BOFs are also simple to implement and can be an important step toward the wide-scale deployment of IMU-based indoor positioning systems.
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