Open Aided Navigation
This open source project aims to demonstrate and explain state of the art methods of modern aided navigation and multi-sensor localization. The software provided in the repository is written in Matlab. Nevertheless, principal architecture of the sensor fusion software tries to ensure simple transferability to industrial software projects.
Simulation of ideal IMU measurements and reference trajectory
Going through the demo you will learn how to generate ideal IMU measurements from position and orientation timeseries provided by a vehicle simulator or measured by some navigation system in a field test. IMU data, generated by the provided software, allows to reconstruct reference trajectory by means of inertial navigation approach almost exactly.
GPS least-squares positioning
This examples shows how to derive user's position from raw measurements of a GNSS receiver.
GPS Kalman filter based position, velocity, and time (PVT) estimation
The demo shows how to use a Kalman filter for position, velocity, and time estimation. The Kalman filter based approach has several advantages compared the single-epoch least-squares method: smoother result, ability to provide navigation information during short outages of GNSS signal reception.
Automotive loosely-coupled INS/GNSS sensor fusion
This example demonstrates how to do fusion of an Inertial Navigation System (INS) and GNSS position information in an automotive application.