Researchers in academics and companies working on location-based services (LBS) are paying close attention to indoor localization based on pedestrian dead reckoning (PDR) because of its infrastructure-free localization method. PDR is the fundamental localization technique that utilize human motion to perform localization in a relative sense with respect to the initial position. The size, weight, and power consumption of micromechanical systems (MEMS) embedded into smartphones are remarkably low, making them appropriate for localization and positioning. Traditional pedestrian PDR methods predict position and orientation using stride length and continuous integration of acceleration in step and heading system (SHS)-based PDR and inertial navigation system (INS)-PDR, respectively. However, these two approaches provide accumulations of error and do not effectively leverage the inertial measurement unit (IMU) sequences. The PDR navigation solution relays on the standard of the MEMS, which yields PDR with the acceleration and angular velocity from the accelerometer and gyroscope, respectively. However, low-cost small MEMSs endure enormous error sources such as bias and noise. Hence, MEMS assessments lead to navigation solution drifts when utilized as inputs to the PDR. As a consequence, numerous methods have been proposed to mitigate and model the errors related to MEMS. Deep learning-based dead reckoning algorithms are provided to address aforementioned issues owing to the end-to-end learning framework. This paper proposes a hybrid convolutional neural network (CNN) and long short-term memory network (LSTM)-based inertial PDR system that extracts inertial measurement units (IMU) sequence features. The end-to-end learning framework is introduced to leverage the efficiency of low-cost MEMS because data-driven solutions provide more complete knowledge of the ever-increasing data volume and computational power over the filtering model approach. A CNN-LSTM model was employed to capture local spatial and temporal features. Experiments conducted on odometry datasets collected from multi-sensor backpack devices demonstrated that the proposed architecture outperformed previous traditional PDR methods, demonstrating that the root mean square error (RMSE) for the best user was 0.52 m. On the handheld smartphone-only dataset the best achieved R² metric was 0.49.

Accurate alignment of real-world object poses with their virtual counterparts using sensors, e.g. cameras, is essential for consistent interaction in mixed-reality systems. However, objects can undergo abrupt, untracked movements during periods when a tracking system is inactive, e.g., overnight, causing stored pose records to become inconsistent with the real scene and breaking user interaction in the virtual environment. Off-the-shelf 3D reconstruction networks such as MASt3R (Matching and Stereo 3D Reconstruction) method provide metrically scaled 3D point maps and pixel correspondences, but they are trained on static scenes and therefore fail to produce reliable object correspondences when the object has moved. We propose a robust pipeline that combines MASt3R’s metrically scaled 3D outputs with a background-based alignment strategy to recover and apply the true pose change of moved objects. Our method first segments foreground and background and extracts 3D background point sets for a reference day and a current day. An affine transformation between these background point sets is estimated via a standard registration technique and used to express the current-day object 3D coordinates in the reference coordinate frame. Within that unified frame we compute the object pose change and apply the resulting transform to the virtual object, restoring real–virtual consistency. Experiments on real scenes demonstrate that the proposed approach reliably corrects pose misalignments introduced during inactive periods and substantially improves over applying MASt3R alone, thereby enabling restored and consistent user interaction in the virtual environment.