Skip to content

A lightweight setero visual SLAM system implementation, including complete closed-loop detection, front-end tracking, back-end optimization, visualization and other parts.

Notifications You must be signed in to change notification settings

weihaoysgs/ssvio

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

44 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Simple Stereo Visual Inertial SLAM(SSVIO)

A lightweight setereo visual-inertial SLAM system implementation, including complete closed-loop detection, front-end tracking, back-end optimization, visualization and other parts. This warehouse is more friendly to students who are new to SLAM. At the same time, the performance of this system is evaluated on the Kitti dataset. Although there is still a certain distance from the SOTA method, it is basically a usable visual odometry system.You can see a more detailed running effect of the entire project on bilibili.

Install 🛠️

  • Opencv3.2 needs to be installed in advance, of course, its 3.x version should also be applicable.
  • At the same time we use Pangolin0.6 for visualization.
sudo apt install libgoogle-glogdev libeigen-dev libsuitesparse-dev libcholmod3
cd thirdparty/g2o
mkdir build
cmake ..
make -j

cd ../DBoW2
mkdir build
cd build
cmake ..
make -j
  • Next compile our project
mkdir build && cd build
cmake ..
make -j

Running SSVIO 🏃

You need to pass in two parameters via the command line according to the glog, the example as follow

../bin/test_system \
--config_yaml_path=/home/xxx/ssvio/config/kitti_00.yaml \
--kitti_dataset_path=/home/xxx/kitti/dataset/sequences/00

System Result On Kitti Dataset🏋️

We verified the SLAM algorithm in this warehouse on the Kitti dataset, and compared the results with and without loop closure, the evaluate tool is evo tool ,as shown below.

Without Loop On Kitti 00 Seq With Loop On Kitti 00 Seq

Citation 📝

TODO📜

  • Update Pangolin UI
  • Add IMU to backend opt
  • Embed LightGlue into the front end

About

A lightweight setero visual SLAM system implementation, including complete closed-loop detection, front-end tracking, back-end optimization, visualization and other parts.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published