ROCIP: robust continuous inertial position tracking for complex actions emerging from the interaction of human actors and environment.

Inertial navigation is advancing rapidly due to improvements in sensor technology and tracking algorithms, with consumer-grade inertial measurement units (IMUs) becoming increasingly compact and affordable. Despite progress in pedestrian dead reckoning (PDR), IMU-based positional tracking still faces significant noise and bias issues. While traditional model-based methods and recent machine learning approaches have been employed to reduce signal drift, error accumulation remains a barrier to long-term system performance. Inertial tracking's self-contained nature offers broad applicability but limits integration with a global reference frame. To solve this problem, a system that could "introspect its error" and "learn from the past" is proposed. It consists of a neural statistical motion model that regresses both poses and uncertainties with DenseNet, which are then fed into Rao-Blackwellised particle filter (RBPF) for calibration with a probabilistic transition map. An inertial tracking dataset with head-mounted IMUs was collected, including walking and running with different speeds while allowing participants to rotate their heads in a self-selected manner. The dataset consisted of 19 volunteers that generated 151 sequences in 4 scenarios with a total time of 929.8 min. It was shown that our proposed method (ROCIP) outperformed the leading methods in the field, with a relative trajectory error (RTE) of 4.94m and absolute trajectory error (ATE) of 4.36m. ROCIP could also solve the problem of error accumulation in dead reckoning and maintain a small and consistent error during long-term tracking.