Pedestrian Dead Reckoning a.k.a. Indoor Localization using IMU (Inertial Measurement Unit)

Xception-GRU with attention for trajectory estimation. (Indoor Localization using RSS + IMU)

real-time indoor child localization system for a child wearing a BW16 embedded device is tracked continuously parents receive live position updates on a Flutter mobile application. The system fuses one-sided Wi-Fi RTT ranging with IMU-based pedestrian dead reckoning (PDR) through an Extended Kalman Filter (EKF)

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A probabilistic Pedestrian Dead Reckoning (PDR) system for indoor localization. Implements Kalman Filtering for sensor fusion, along with Bayesian grid-based methods and Particle Filtering to mitigate IMU drift and achieve accurate positioning in GPS-denied environments.

GeoSense is a lightweight, GPS-free localization system using IMU, magnetometer, and Wi-Fi RSSI with adaptive sensor fusion and online learning. Designed for indoor and GPS-denied environments, it delivers real-time, self-improving positioning on embedded devices.

This research investigates how noise filtering techniques in UWB systems, combined with sensor fusion, can enhance indoor localization accuracy. By integrating data from sensors like Inertial Measurement Units (IMUs) and applying advanced filtering techniques such as Kalman Filters, the study aims to improve positioning precision.

A real-world indoor ROS 2 dataset acquired with TurtleBot 4 at the Higher School of Technology of Salé (UM5 Rabat). It includes LiDAR, odometry, IMU, RGB data, TF frames, SLAMToolbox maps, ROS 2 bag files, CSV exports, and videos, supporting SLAM, localization, navigation, and education.