RTK-EVC: Real-Time Kinematic Positioning with Enhanced Velocity Constraints via Factor Graph Optimization in GNSS-challenged Environments

We open-source the source code and experiment dataset of a novel Real-time Kinematic positioning architecture with Enhanced Velocity Constraints (RTK-EVC) via Factor Graph Optimization in GNSS-challenged Environments. The related paper has been submitted to IEEE for possible publication. If you use the contents of this repository in your work, please cite the preprint of our paper as follows:

@article{zhao2025rtk,
  author = {Zhao, Qijia and Yan, Sudan and Liu, Gang and Zhang, Rong and Lü, Shaolin and Lou, Jianan},
  title = {{RTK}-{EVC} : {Factor} {Graph} {Optimization} {Based} {Real}-time {Kinematic} {Positioning} with {Enhanced} {Velocity} {Constraints} in {GNSS}-chanllenged {Environments}},
  journal = {TechRxiv},
  year = {2025},
  month = jan,
  doi = {10.36227/techrxiv.173747652.25780044/v1}
}

How to Use This Library

The library was developed based on RTKLIB 2.4.3 b34 and tested in Ubuntu 20.04. The results may be different for different OS. If any problem was found by you, please propose an issue or report it to [email protected].

Requirements

1. Ubuntu 20.04 with the newest compiler is recommended.
2. Eigen3
3. Ceres Solver

Eigen 3.3.7 and Ceres 2.1.0 have been tested and work properly. The dataset path and config path need to be modified in src/global/global.cpp. The trace log path need to be modified in src/main.cpp.

Clone the repository

git clone https://github.com/zhaoqj23/RTK-EVC.git

Download the dataset

The dataset of RTK-EVC can be obtained via Onedrive or BaiduCloud. The experiment was carried out in Beijing, China, around Tsinghua University and Zhongguancun, with multiple GNSS-challenged scenarios. IMU-ISA-100C and Inertial Explorer were used to get the ground truth.

Build the library

cd ~/RTK-EVC
mkdir build && cd build
cmake ..
make -j8

Run demo

cd ~/RTK-EVC
./bin/RTK-EVC