II-Researcher: a new open-source framework designed to aid building search / research agents

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.

Implementation and evaluation of an indoor localization system based on Wi-Fi signal strength (RSSI).

This repository contains the tools and results developed for my undergraduate thesis (TCC) in Computer Science at UFES São Mateus. The project focuses on analyzing and improving Wi-Fi coverage on campus through network mapping, custom measurement tools, and advanced router placement simulations. All solutions are data-driven and open-source.

An autonomous mobile robot that navigates using ROS 2, AMCL, and LiDAR.

Tsetlin Machine-based indoor localization using BLE RSSI fingerprinting.

System for Tracking the Indoor Location of Bluetooth Low Energy (BLE) Beacons Using ESP32.

This repository serves as a core integration for a primitive indoor localization system, developed as part of a Smart City graduation project. The system enables an agent (simulating a vehicle) to navigate on a map toward a designated target point smoothly after determining its current position.

GEO1101 Synthesis Project from MSc Geomatics TU Delft

Indoor Beacon Tracking System based on BLE

A real-time person tracking system using AprilTags.

Everyday Helper, Things Locator, Safety and Support for Special Needs

Wi-Fi positioning system that approximates user device location on a map using signal strength from nearby Wi-Fi access points.

Obstruction-Aware Signal-Loss-Tolerant Indoor Positioning (OASLTIP) Using Bluetooth Low Energy

Indoor Positioning System Using Bluetooth RSSI and Trilateration. ESP32's as receivers.

Accurately track the location of students seated in a classroom at the seat level.

An application that can track your position based on nearby router signals. An android application and a Flask server work together to gather, analyse and output data.

Extracts low speed segments from spatiotemporal trajectories using moving median of speed. Fast and robust. Adaptively determines the parameters from the data, instead of setting objective, arbitrary parameters. Each trajectory in a set of trajectories will have unique subjective parameters.

Uses WiFi signals 📶 and machine learning to predict where you are

This is ros package for PX4 autoplot with remote control, offboard mode, Slam autonomous navigation...