A lightweight setero visual-inertial SLAM system implementation, including complete closed-loop detection, front-end tracking, back-end optimization, visualization and other parts.
A lightweight setero 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.
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
mkdir build && cd build
cmake ..
make -j
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
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.