High-precision indoor localization using the extended Kalman filter approach Conferences uri icon

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abstract

  • Indoor positioning and navigation have emerged as critical areas of research due to the limitations of GPS in enclosed environments. This study presents an innovative approach to high-precision indoor localization by employing the Extended Kalman Filter (EKF). Unlike traditional methods that often suffer from noise and multi-path effects, the EKF methodology accounts for nonlinearities and offers a recursive solution to estimate the state of dynamic systems. We deployed a sensor on a mobile robot that needs to move in an indoor environment while there is a moving obstacle that is moving around. Our findings demonstrate a significant accuracy in locating the obstacle while maneuvering inside the environment.

publication date

  • June 5, 2024