Tip
Check out the presentation video above for a quick overview of this work!
We warmly welcome and highly recommend the integration of the Ground-Fusion system into your projects for several compelling reasons:
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🔥Comprehensive Sensor Suite: The Ground-Fusion system is equipped with a multitude of sensors (RGBD-IMU-Wheel-GNSS), facilitating an easy onset for enhancements to any module. This richness in sensory input streamlines the process of adapting and refining components within the system.
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⭐️Open-Source Ecosystem: Both the Ground-Fusion algorithm and associated datasets such as M2DGR-plus and the Ground-Challenge are openly available, forming a comprehensive bechmark suite. Welcome to beat Ground-Fusion on M2DGR and Ground-Challenge!
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🚀Proven Performance: The Ground-Fusion algorithm has been rigorously validated across diverse datasets, establishing itself as SOTA in lidar-less SLAM algorithms. Outperforming Ground-Fusion on these benchmarks would significantly bolster the credibility of your proposed method.
We introduce Ground-Fusion, a low-cost sensor fusion simultaneous localization and mapping (SLAM) system for ground vehicles. Our system features efficient initialization, effective sensor anomaly detection and handling, real-time dense color mapping, and robust localization in diverse environments. We tightly integrate RGB-D images, inertial measurements, wheel odometer and GNSS signals within a factor graph to achieve accurate and reliable localization both indoors and outdoors. To ensure successful initialization, we propose an efficient strategy that comprises three different methods: stationary, visual, and dynamic, tailored to handle diverse cases. Furthermore, we develop mechanisms to detect sensor anomalies and degradation, handling them adeptly to maintain system accuracy.
The preprint version of paper is here. The dataset is at M2DGR-plus, Ground-Challenge and M2DGR.
Figure 1. We categorize corner cases into three types: visual, wheel, and GNSS challenges.
Tested on Ubuntu 18.04 (with ROS Melodic and OpenCV3) and on Ubuntu 20.04(with ROS Noetic and OpenCV4).
This package requires OpenCV 3/4 and some features of C++11.
This package requires Eigen 3.3.7, Ceres 1.14,Sophus and PCL 1.10 or 1.11. You need to download they in your thirdparty folder, and then:
sudo apt-get update
sudo apt-get install -y cmake libgoogle-glog-dev libgflags-dev libatlas-base-dev libsuitesparse-dev
cd thirdparty/eigen
mkdir -p build && cd build
cmake ..
sudo make install
cd ../../ceres-solver
mkdir -p build && cd build
cmake ..
make -j$(nproc)
sudo make install
sudo apt-get install -y libflann-dev libvtk6-dev libboost-all-dev ros-noetic-pcl-ros (for ubuntu20.04) libfmt-dev
cd ../../pcl
mkdir -p build && cd build
cmake ..
make -j$(nproc)
sudo make install
cd ../../Sophus
mkdir -p build && cd build
cmake ..
make -j$(nproc)
sudo make install
This package also requires gnss_comm for ROS message definitions and some utility functions.
sudo apt-get install g++-8
sudo apt-get install gcc-8
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-8 20
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-8 20
After install all 3rd parties：
mkdir -p ~/Groundfusion_ws/src
cd ~/Groundfusion_ws/src
git clone https://github.com/HKUST-Aerial-Robotics/gnss_comm
git clone https://github.com/SJTU-ViSYS/Ground-Fusion
cd ../..
catkin_make -j12
Tip
If you have problems with Sophus version, try to build and install both template version and non-template version to make sure it works.
Download Ground-challenge dataset and give a star.
# [launch] open a terminal and type:
source devel/setup.bash
roslaunch vins groundfusion.launch
# [run localization] open another terminal:
source devel/setup.bash
rosrun vins vins_node src/Ground-Fusion/config/realsense/groundchallenge.yaml
# [dense map]open third terminal:
source devel/setup.bash
rosrun dense_map dense_map_node src/Ground-Fusion/config/realsense/groundchallenge.yaml
# [play rosbag]open forth terminal:
rosbag play office3.bag
Tip
The dense mapping node may consume computing resources, affecting the real-time performance of the entire system. So it's suggested that do not run this node unless necessary. We are working on optimizing the mapping node currently.
Download M2DGR-Plus dataset and give a star.
# [launch] open a terminal and type:
source devel/setup.bash
roslaunch vins groundfusion.launch
# [run localization] open another terminal:
source devel/setup.bash
rosrun vins vins_node src/Ground-Fusion/config/realsense/m2dgrp.yaml
# [dense map]open third terminal:
source devel/setup.bash
rosrun dense_map dense_map_node src/Ground-Fusion/config/realsense/m2dgrp.yaml
# [play rosbag]open forth terminal:
rosbag play anamoly.bag
[!Know Issues] On M2DGR-plus, Ground-Fusion performs even better without GNSS measurements due to low frequency of GNSS (1Hz) of M2DGR-plus dataset. Welcome to test Ground-Fusion on other datasets suporting RGBD-IMU-Wheel-GNSS settings. The threshold of diffenrent initilization methods need to be adjusted in case that Ground-Fusion faces a drift in some sequences during the initialization phase. Furthermore, we are currently developing a more advanced version of Ground-Fusion, please follow us.
Thanks support from National Key R&D Program (2022YFB3903802), NSFC(62073214), and Midea Group. This project is based on GVINS, and has borrowed some codes from open-source projects VIW-Fusion and VINS-RGBD, thanks for your great contribution!
The source code of Ground-Fusion is released under GPLv3 license. Do not use this project for any commercial purpose unless permitted by authors. Yin Jie is still working on improving the system. For any technical issues, please contact him at [email protected].
If you use this work in an academic work, please cite:
@article{yin2021m2dgr,
title={M2dgr: A multi-sensor and multi-scenario slam dataset for ground robots},
author={Yin, Jie and Li, Ang and Li, Tao and Yu, Wenxian and Zou, Danping},
journal={IEEE Robotics and Automation Letters},
volume={7},
number={2},
pages={2266--2273},
year={2021},
publisher={IEEE}
}
@inproceedings{yin2023ground,
title={Ground-challenge: A multi-sensor slam dataset focusing on corner cases for ground robots},
author={Yin, Jie and Yin, Hao and Liang, Conghui and Jiang, Haitao and Zhang, Zhengyou},
booktitle={2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)},
pages={1--5},
year={2023},
organization={IEEE}
}
@INPROCEEDINGS{yin2024ground,
author={Yin, Jie and Li, Ang and Xi, Wei and Yu, Wenxian and Zou, Danping},
booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
title={Ground-Fusion: A Low-cost Ground SLAM System Robust to Corner Cases},
year={2024},
volume={},
number={},
pages={8603-8609},
keywords={Location awareness;Visualization;Simultaneous localization and mapping;Accuracy;Wheels;Sensor fusion;Land vehicles},
doi={10.1109/ICRA57147.2024.10610070}}