With the continuous advancement of urban construction, inertial sensors are increasingly used in the detection of underground pipelines. However, inertial measurement units (IMUs) are susceptible to a variety of error sources, leading to the accumulation of position estimation errors over time, which severely restricts their positioning accuracy. This paper provides a systematic review of IMU calibration and drift suppression error compensation methods applicable to underground pipeline environments. Building upon this foundation, it innovatively proposes a three-tiered review framework based on “error characteristics–compensation mechanisms–application scenarios”. The framework begins with the characterization of error factors, maps them to corresponding compensation mechanisms, and then applies them to specific operating conditions. It identifies current research limitations in real-time performance, robustness, experimental validation, and standardized evaluation. Future efforts should focus on designing lightweight fusion algorithms, integrating deep learning with sensor fusion, and establishing standardized testing platforms. This paper aims to summarize the current state and development trends of inertial sensor error compensation methods, providing a reference for advancing related technologies while offering beginners a clear and systematic learning path.

Self-Supervised Learning (SSL) has emerged as an effective paradigm for reducing the dependence of Human Activity Recognition (HAR) models on labeled data. To address the inadequate exploitation of IMU spatio-temporal correlations during pre-training and the limited generalization caused by simplistic fine-tuning strategies, a novel SSL framework for IMU-based HAR is proposed. The framework employs the Transformer and Depthwise Separable Convolution (DSC) to jointly capture global temporal dependencies and local spatial features, which are adaptively fused into discriminative spatio-temporal representations. These representations are subsequently enhanced through spatio-temporal feature extraction and multi-dimensional feature aggregation for downstream HAR. Furthermore, an IMU-based data acquisition platform was developed to construct the CQXY dataset. The proposed method was validated through comprehensive evaluations on four public datasets (UCI, Motion, HHAR, and Shoaib) and one self-collected dataset (CQXY). Experimental results show that, on the public datasets, the proposed method improves classification accuracy, F1-score, and Cohen’s kappa coefficient by an average of 13.11%, 14.24%, and 16.70%, respectively, compared with the baseline models. Similarly, on the self-collected dataset, the corresponding improvements reach 8.87%, 11.07%, and 10.81%. These results confirm the generalization of the proposed approach across datasets of different scales and domain.