An indoor positioning system (IPS) is a system to locate objects or people inside a building using radio waves, magnetic fields, acoustic signals, or other sensory information.

This is an open-source project that employs deep learning to enhance indoor positioning using WiFi Channel State Information (CSI). This project aims to harness the detailed data from CSI, such as signal amplitude, phase, and environmental properties, to accurately locate devices inside buildings where GPS is ineffective.

Robotic Localization with SLAM on Raspberry Pi integrated with RP LIDAR A1. Point Cloud remote visualization doing using MQTT in real-time.

The official code and datasets for "Zero-Shot Multi-View Indoor Localization via Graph Location Networks" (ACMMM 2020)

Library for Indoor Positioning purposes

Implementation of the paper Using Synthetic Data to Enhance the Accuracy of Fingerprint-Based Localization: A Deep Learning Approach (IEEE Sensors Letters, 2020), which utilizes Generative Adversarial Networks (GANs) for indoor localiztaion.

Pose Correction for Highly Accurate Visual Localization in Large-scale Indoor Spaces (ICCV 2021)

Useful tools and software for high-precision positioning of low-cost smartphone

Kindona is a flutter-based application for helping users orientate inside of buildings. It provides the user with a map (like google maps and the like), with the option to differentiate between the different floors inside the building.

Automated radio map construction using a Thymio II and a Software-Defined Radio (SDR)

Taha

Development and testing of an indoor positioning system using UWB

Autonomous Indoor Drone with Simulation and Hardware-Setup using ROS and SLAM

Using WiFi signals (RSSI values) to predict indoor locations

Source code for M.T. Hoang, B. Yuen, X. Dong, T. Lu, R. Westendorp and K. Reddy, “Recurrent Neural Networks for Accurate RSSI Indoor Localization,” IEEE Internet of Things Journal, 2019

Augmented Reality-based Indoor Navigation Application is an innovative application designed to assist users unfamiliar with complex buildings such as hospitals, universities, super malls, airports, and railway stations. It guides users to their desired destination, saving both time and energy. This system is cost effective, easy to use.

Indoor Localization via BLE Beacons: A Deep Learning Approach

The source files accompanying our research paper titled - "A Robust Approach for Improving the Accuracy of IMU based Indoor Mobile Robot Localization".

This repository contains source code implementation of projects for NTU's MSAI course AI6128 on Urban Computing (2020 Sem 1).

Dataset for ESP32C3 WiFi FTM RSSI Indoor Localization