Open Aided Navigation

Introduction

This is a repository of the Open Aided Navigation project started by Fedor Baklanov. The project aims to demonstrate and explain state of the art methods of modern aided navigation and multi-sensor localization. The software provided in this repository is written in Matlab for the sake of simplicity. Nevertheless, principal architecture of the sensor fusion software tries to ensure simple transferability to industrial software projects.

If you would like to contribute, you are welcome to get in touch with us by sending an email to [email protected].

Available examples

Overview

The following examples/demonstrations are currently available:

1. Simulation of ideal IMU measurements and reference trajectory
2. GPS least-squares based position and velocity estimation
3. GPS Kalman filter based position, velocity, and time (PVT) estimation
4. Automotive loosely-coupled INS/GNSS sensor fusion

See below for details.

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. In fact, accuracy of the reconstructed position, velocity, and orientation will be limited only by precision of the chosen numerical integration method and step size.

Path to the demo: demo\imuSimulation\sim_imuAndRef.m

Required Matlab toolboxes: Curve Fitting toolbox.

Explanation of the algorithm: demo\imuSimulation\Documentation_IMU_Simulation.pdf

How to start

1. Open the file demo\imuSimulation\sim_imuAndRef.m in Matlab
2. Run the script, it should work in one click!

GPS least-squares positioning

This example shows how to derive user's position from raw measurements of a GNSS receiver. The script supports .nex input files collected by the app Nav Sensor Recorder. You can find out more more about Navigation Sensor Data Exchange format under this link.

Path to the demo: demo\gnssLsq\script_runGnssAlgo_nex.m

How to start

1. Open the file demo\gnssLsq\script_runGnssAlgo_nex.m in Matlab
2. Run the script, it should work in one click!

GPS Kalman filter based position, velocity, and time (PVT) estimation

The demonstration 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. The script supports .nex input files collected by the app Nav Sensor Recorder. You can find out more more about Navigation Sensor Data Exchange format under this link.

Path to the demo: demo\gnssPvt\run_gnssPvtFilter_nex.m

How to start

1. Open the file demo\gnssPvt\run_gnssPvtFilter_nex.m in Matlab
2. Run the script, it should work in one click!

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. The script uses .csv logs of accelerometer, gyroscope, and GNSS measurements collected by the app Nav Sensor Recorder.

Path to the demo: demo\insGnssLoose\run_insGnssLoose.m

Algorithm description: demo\insGnssLoose\Documentation_InsGnssFilterLoose.pdf

Disclaimer

The provided software aims to demonstrate published state-of-the-art sensor fusion techniques. The provided sensor fusion algorithm does not present a ready-to-use solution for any kind of application.

How to start

1. Open the file demo\insGnssLoose\run_insGnssLoose.m in Matlab
2. Run the script, it should work in one click!