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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\u2019s self-contained nature offers broad applicability but limits integration with a global reference frame. To solve this problem, a system that could \u201cintrospect its error\u201d and \u201clearn from the past\u201d 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.<\/jats:p>","DOI":"10.1007\/s10489-025-06409-1","type":"journal-article","created":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T03:11:51Z","timestamp":1740798711000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["ROCIP: robust continuous inertial position tracking for complex actions emerging from the interaction of human actors and environment"],"prefix":"10.1007","volume":"55","author":[{"given":"Xinyu","family":"Hou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeroen","family":"Bergmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,1]]},"reference":[{"key":"6409_CR1","doi-asserted-by":"crossref","unstructured":"Beuchert J, Camurri M, Fallon M (2023) Factor graph fusion of raw gnss sensing with imu and lidar for precise robot localization without a base station. 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