Practical Implementation of KalmanNet for Accurate Data Fusion in Integrated Navigation

Flow Chart

2012-11-16 Result on Map

Environment Setup

pip install -r requirements.txt

Data Preprocessing

1. We have provided data in the ./data/NCLT/processed/ that has been preprocessed (consistent with the paper).
2. If you want to re-download the data and process it, use the following steps

# download sensor data and ground truth
python ./data/NCLT/down.py --sen 
python ./data/NCLT/down.py --gt 
ls ./data/NCLT/download/sensor_data/*.tar.gz | xargs -n1 tar xzvf
python ./data/NCLT/preprocess.py

Train and Predict

1. Train.

# KalmanNet
python train.py --cfg ./configs/nclt/fusion/wheel_gpsfusion_origin.py
# Split KalmanNet
python train.py --cfg ./configs/nclt/fusion/wheel_gpsfusion_split.py

2. Inference.
   Reference nclt_predict.ipynb

Plotting on Map

We recommend using QGIS for visualization on maps. If you only want to see our results: Using QGIS to open ./QGIS/20121116.qgz. Else if you want to plot your results:

1. Using the code provided at the bottom of nclt_predict.ipynb, the coordinates are converted and saved into kml (Code is modified from https://github.com/AbhinavA10/mte546-project).
2. Open QGIS and load vector(xxx.kml)