Estimating velocity and position from an IMU is feasible, but it is fundamentally a drift-limited problem. Velocity is obtained by integrating acceleration once, and position by integrating twice, so small errors in sensor bias, orientation, timing, and gravity compensation grow rapidly over time. In practice, useful results require a full inertial navigation pipeline: sensor calibration, attitude estimation (gyro/accel/magnetometer fusion), gravity compensation, and drift mitigation (for example ZUPT for foot-mounted systems, motion constraints, or fusion with external aids such as GNSS, vision, or UWB). For this reason, reliable velocity/position estimation is usually treated as a sensor-fusion problem rather than an IMU-only computation.
- Bebek, O.; Suster, M.A.; et al., "Personal Navigation via High-Resolution Gait-Corrected Inertial Measurement Units," Instrumentation and Measurement, IEEE Transactions on , vol.59, no.11,
doi: 10.1109/TIM.2010.2046595 - Foxlin, E., "Pedestrian tracking with shoe-mounted inertial sensors," Computer Graphics and Applications, IEEE , vol.25, no.6, pp.38,46, Nov.-Dec. 2005. doi: 10.1109/MCG.2005.140
- Manon Kok, Jeroen D. Hol and Thomas B. Sch¨on (2017), ”Using Inertial Sensors for Position and Orientation Estimation”, Foundations and Trends in Signal Processing: Vol. 11: No. 1-2, pp 1-153.
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