MTE 546 Final Project

Extended Kalman Filter algorithm to globally localize a robot from the University of Michigan's North Campus Long-Term Vision and LIDAR Dataset.

The EKF performs sensor fusion of IMU, Wheel Velocities, and Low-quality GPS data to estimate the 2D pose of the mobile robot. We acheive accuracy similar to that of GPS-RTK outdoors, as well as positional estimates indoors.

See our paper for more.!

EKF estimate for "Wheels with GPS" mode for 2013-04-05 path. Blue: Estimated Position. Red: Ground Truth Position

EKF estimate for "Wheels with GPS" mode for 2015-05-11 path. Blue: Estimated Position. Red: Ground Truth Position

Above plot, zoomed in:

EKF Estimation vs Ground Truth over time. Periods of divergence are when the robot looses GPS and travels indoors:

• Download the dataset:
  • Download the specific date desired ( sen.tar.gz and groundtruth.csv files) from the NCLT Dataset and unzip into ./src/dataset/<YYYY-MM-DD>
  • Alternatively, unzip the dataset.zip into ./src/dataset
• pip install matplotlib numpy pandas sympy scipy lxml

From src folder,

• python read_ground_truth.py
• python read_gps.py
• python read_wheels.py
• python read_imu.py
• python IMU_processing.py
• python EKF.py 2013-04-05: Run EKF with config given in EKF.py for the given path
• python run_all.py: Run EKF with config given in EKF.py for all paths in the dataset

The EKF is able to run in different modes, using these parameters:

The following paths do not have readable wheel velocities:

• 2012-01-08
• 2012-01-22
• 2012-02-12
• 2012-03-17
• 2012-05-26
• 2012-06-15

@ARTICLE { ncarlevaris-2015a,
    AUTHOR = { Nicholas Carlevaris-Bianco and Arash K. Ushani and Ryan M. Eustice },
    TITLE = { University of {Michigan} {North} {Campus} long-term vision and lidar dataset },
    JOURNAL = { International Journal of Robotics Research },
    YEAR = { 2015 },
    VOLUME = { 35 },
    NUMBER = { 9 },
    PAGES = { 1023--1035 },
}