Brandon Wagstaff

Ph.D. Student

Brandon is interested in using low-cost sensors (such as cameras or inertial sensors) localization. In particular, he is investigating methods to extract useful information from complex data using machine learning, which can then be integrated into classical state estimation techniques. Ultimately, this work is intended to produce localization algorithms that are able to operate within challenging environments, where classical algorithms are prone to failure.
For example, classical algorithms commonly rely on parameter tuning/calibration, which is highly sensitive to the agent’s motion, or to the environment that the agent operates within. One of my goals is to obviate the need for calibration or parameter tuning by replacing the sensitive components of the system with more robust learning-based models. In doing so, these systems can be employed without requiring time-consuming calibration, and will be able to operate within continuously changing environments over longer periods of time.

Foot-Mounted Inertial Navigation for Indoor Localization

Improving foot-mounted inertial navigation through real-time motion classification
Brandon Wagstaff, Valentin Peretroukhin and Jonathan Kelly
Indoor Positioning and Indoor Navigation Conference (2017).
LSTM-based zero-velocity detection for robust inertial navigation
Brandon Wagstaff and Jonathan Kelly
Indoor Positioning and Indoor Navigation Conference (2018).