MKF
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purewater0901
Moment-based Kalman Filter: Nonlinear Kalman Filtering with Exact Moment Propagation
Moment-based Kalman Filter(MKF) is a nonlinear Kalman filter that uses exact moment propagation method to estimate state from noisy measurements.
Paper
This paper has been accepted by the IEEE Conference on Robotics and Automation (ICRA), 2023.
Language and test environment
The main code is written in C++ and visualization code is mainly used Python. However, we also use matplotlib.cpp for the simple visualization in C++. Note that we only test our code on Ubuntu20.04.
Dataset for the experiment
We use UTIAS Multi-Robot Cooperative Localization and Mapping Dataset to evaluate the MKF.
Requirements
CMake >= 3.22
Getting Started
git clone https://github.com/purewater0901/MKF.git
cd MKF
mkdir build && cd build
cmake ..
make -j8
- Run MKF with Gaussian measurements
./main
- Run MKF with non-Gaussian measurements
Before running the code for non-Gaussian environment, you need to preprocess the dataset. After that you can run the main code.
./data_preprocessor
./main_non_gaussian
Result with UTIAS dataset
Robot1
Robot2
References
[1] "Moment-Based Exact Uncertainty Propagation Through Nonlinear Stochastic Autonomous Systems", Ashkan Jasour, Allen Wang, Brian C. Williams https://arxiv.org/abs/2101.12490
[2] "The UTIAS multi-robot cooperative localization and mapping dataset", Leung, Keith and Halpern, Yoni and Barfoot, Timothy and Liu, Hugh, https://journals.sagepub.com/doi/10.1177/0278364911398404
purewater0901