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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月1日

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  • Xinyu Hou,
  • Jeroen Bergmann

摘要

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.