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https://github.com/cggos/imu_x_fusion
IMU + X(GNSS, 6DoF Odom) Loosely-Coupled Fusion Localization based on ESKF, IEKF, UKF(UKF/SPKF, JUKF, SVD-UKF) and MAP
https://github.com/cggos/imu_x_fusion
ekf-localization eskf gnss imu-sensor nonlinear-least-squares sensor-fusion slam ukf-localization visual-inertial-odometry
Last synced: 7 days ago
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IMU + X(GNSS, 6DoF Odom) Loosely-Coupled Fusion Localization based on ESKF, IEKF, UKF(UKF/SPKF, JUKF, SVD-UKF) and MAP
- Host: GitHub
- URL: https://github.com/cggos/imu_x_fusion
- Owner: cggos
- License: gpl-3.0
- Created: 2020-07-25T15:29:13.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-11-24T07:38:21.000Z (about 1 month ago)
- Last Synced: 2024-12-20T06:04:59.041Z (14 days ago)
- Topics: ekf-localization, eskf, gnss, imu-sensor, nonlinear-least-squares, sensor-fusion, slam, ukf-localization, visual-inertial-odometry
- Language: C++
- Homepage: https://msf.cgabc.xyz/
- Size: 2.36 MB
- Stars: 927
- Watchers: 16
- Forks: 164
- Open Issues: 14
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# IMU + X Fusion
IMU + X Loosely-Coupled Fusion Localization
---
## Overview
* IMU + GPS
- [x] ESKF: [Multi-Sensor Fusion: IMU and GPS loose fusion based on ESKF](https://cgabc.xyz/posts/4e9a780e/)
* IMU + 6DoF Odom (e.g.: Stereo Visual Odometry)
- [x] ESKF: [IMU and 6 DoF Odometry (Stereo Visual Odometry) Loosely-Coupled Fusion Localization based on ESKF (Presentation)](http://dx.doi.org/10.13140/RG.2.2.28797.69602)
- [x] IEKF: https://cgabc.xyz/posts/784a80cb/#iekf
- [x] UKF(including JUKF and SVD-UKF): [IMU and 6 DoF Odometry (Stereo Visual Odometry) Loosely-Coupled Fusion Localization based on UKF](http://dx.doi.org/10.13140/RG.2.2.30602.54727)
- [x] MAP(User-defined L-M, Ceres-Solver, G2O and GTSAM)
## Features
* Dataset
- [x] KAIST dataset: ROS publish
* Sensors
- [x] IMU
- [x] Numerical Integration (TODO: RK4)
- [ ] Pre-Integration
- [ ] Wheel Odometer
- [ ] Manometer
- [x] GPS
- [x] 6DoF Odom (Stereo Visual Odometry)
- [ ] LiDAR
- [ ] Multi-modal data
* State Estimation
- [x] EKF
- [x] ESKF
- [x] IEKF
- [x] UKF
- [x] JUKF
- [x] SVD-UKF
- [ ] Particle Filter
- [x] MAP
- [x] User-defined G-N
- [x] Ceres-Solver
- [x] G2O
- [x] GTSAM
* Local / Global Angular Error (Rotation Perturbation)
- [x] Local Angular Error (OK)
- [x] Global Angular Error (TODO: why Poor)
```cpp
enum ANGULAR_ERROR { LOCAL_ANGULAR_ERROR, GLOBAL_ANGULAR_ERROR };
```
* Debug
- [x] Check Measurement Jacobian Matrix
## Requirements
tested on Ubuntu 16.04, Ubuntu 18.04 and Ubuntu 20.04
* Ubuntu 16.04 & ROS Kinetic
* OpenCV 3
* ROS package: nmea_navsat_driver
* GeographicLib 1.50.1 (built from souce code, cmake 3.18.0 tested)
* Ubuntu 18.04 & ROS Melodic
* OpenCV 3
* ROS package: nmea_navsat_driver
* GeographicLib 1.49
```sh
sudo apt install libgeographic-dev
```
* Ubuntu 20.04 & ROS Melodic
* OpenCV 3
* ROS package: nmea_navsat_driver
* GeographicLib 1.50.1
```sh
sudo apt install libgeographic-dev
```
## Build
```sh
mkdir -p ws_msf/src
cd ws_msf/src
git clone xxx
cd ..
catkin_make -j4 # error happened when using the default cmake 3.5.1 on Ubuntu 16.04, upgrade it
# or
catkin build -j4
```
## Run on Host
### IMU + GNSS
test data: [utbm_robocar_dataset_20180719_noimage.bag](https://lcas.lincoln.ac.uk/owncloud/index.php/s/KfItDFgwwis5Xrk)
* [sensor_msgs/Imu] /imu/data: 100 hz
* [nmea_msgs/Sentence] /nmea_sentence: 15 hz
* [sensor_msgs/NavSatFix] /fix: 5 hz
* [nav_msgs/Path] /nav_path: 63 hz
```sh
roslaunch imu_x_fusion imu_gnss_fusion.launch
rosbag play -s 25 utbm_robocar_dataset_20180719_noimage.bag
```
ROS graph and path on rviz:
plot the result path (fusion_gps.csv & fusion_state.csv) on Google Map using the scripts `folium_csv.py`:
### IMU + 6DoF Odom
#### VO: ORB-SLAM2 (Stereo) + EuRoC V1_01_easy.bag
```sh
roslaunch imu_x_fusion imu_vo_fusion.launch [est:=ekf, ukf or map]
```
##### run ORB-SLAM2 (Stereo) and play back bag file
```sh
# https://github.com/cggos/orbslam2_cg
# pose cov:
# sigma_pv: 0.001
# sigma_rp: 0.5
# sigma_yaw: 0.5
roslaunch orbslam2_ros run_stereo_euroc.launch
rosbag play V1_01_easy.bag
```
results(Green path: estimated pose; Red path: pose of VO):
##### Use the recorded bag file directly
Download [orbslam2_v101easy.bag](http://gofile.me/5lGth/4XBQVLhAn)
```sh
rosbag play orbslam2_v101easy.bag
```
#### VO: ORB-SLAM2 (Stereo) + MYNTEYE-S1030 Camera
```sh
# TODO: Test
roslaunch imu_x_fusion imu_vo_fusion_mynteye.launch
roslaunch mynt_eye_ros_wrapper mynteye.launch
```
### Run with KAIST Dataset
```sh
rosrun imu_x_fusion kaist_pub /dev_sdb/datasets/KAIST/urban39-pankyo
```
## Run with Docker
```sh
# pull from DockerHub
sudo docker pull cggos/ubuntu-ros-slam:bionic-melodic
# run, e.g.: imu_vo_fusion
./scripts/run_docker.sh
# modify the script for running others
```
## Code Format
code format based on Google style
```sh
./batch_format.sh
```
## Docs
* Requirements
```sh
sudo apt install graphviz
sudo apt install doxygen
```
* Generate
```sh
# output in html and latex dir
doxygen Doxygen.config
```
## Community
* Welcom to join [the Discord channel #multi-sensor-fusion](https://discord.gg/2RV2vKmdpa)