Navigation (EKF) toolbox with python wrappers. Suitable for use in small UAS applications. The code is structured with dual C++ and python interfaces. It includes a plotting librar for comparing filters or configurations.

This code project was original put together by Hamid Mokhtarzadeh mokh0006 at umn dot edu in support of the research performed by the UAS and Control Systems groups at the Aerospace Engineering and Mechanics Deptarment, University of Minnesota.

• Aura text format

• UMN .mat (matlab, hdf5)

• Ardupilot tlog (partial support, I would be happy to find a volunteer to improve this.)

• PX4 sdlog2_dump and ulog2csv formats (CSV).

• Sentera camera IMU format

• 15 state EKF using only gyro, accels, and gps for input. Converges to true heading without needing magnetometers.

• 15 state EKF that includes magnetometers in the measurement update. More stable in attitude, but assumes a quality magnetometer calibration.

• Piece-wise segment optimizer.

• Run two filters (or the same filter with different noise settings) and plot the results side by side.

• Core filters are written in C/C++ but the infrastructure, data loading, and plotting is handled in python.

• Uses boost/python so that the same core C++ code can be used from either C++ or python applications.

• Includes code that can import a flight data set and do a least squares fit of an ellipsoid to the magnetometer data to callibrate the magnetometer.

• Includes code that can compare the expected ideal mag vector (based on location, date, and aircraft orientation) versus the actual sensed mag vector and do a best fit (mag calibration) from flight data.