Nicholas Gurnard, Aadit Patel, Pranav Shah
pytorch, numpy, matplotlib, sklearn, tqdm, click, scipy, cv2 (opencv-python), PyYAML
pip install torch
pip install numpy
pip install matplotlib
pip install sklearn
pip install tqdm
pip install click
pip install scipy
pip install opencv-python
pip install PyYAML
It is required that you first download the EuRoC MAV datasets in order to run the filters. EuRoC
The data must be placed in the directory ./data/euroc_mav_dataset/. The files within this dataset must be named MH_01_easy, MH_02_easy, MH_03_medium, MH_04_difficult, MH_05_difficult in correspondence to the EuRoC dataset naming conventions.
There are 2 scripts to be run from the root: train.py and test_and_plot.py.
These 2 scripts are the main part of the project.
Running the flag --help will give instruction on how to run the code with the given datasets and which models to run. Example:
test_and_plot.py:
python test_and_plot.py --dataset=3 --ensemble=False
train.py:
python train.py --dense=False
In order to generate the datasets to run the above scripts, you can run the filters individually. The files are already provided, but in case you want to run them on new datasets you must run the following:
UKF:
python ukf/estimate_rot.py
In order to run different datasets, change line 18 dirname to point to the correct data
ESKF:
python eskf/sandbox_vio.py
In order to run different datasets, change line 21 dirname to point to the correct data
CF:
python complimentary/sandbox_complementary_filter.py
In order to run different datasets, change line 16 fname to point to the correct data
UKF - Aadit Patel ESKF - Pranav Shah CF - Nicholas Gurnard Ensemble - equal Contributions Report- equal Contributions