TurtleZhong/ov_hloc

using hloc for loop closure in SLAM

OV_HLOC

Using Hierarchical-Localization instead of DBoW2 for loop closure. This was originally part of my undergraduate final project, where I worked on improving the loop closure module of VINS-Fusion. I found it just right for providing a loosely coupled pose graph for OpenVINS.

Thanks to the excellent global pose graph optimization provided by VINS-Fusion, this project performs well on the EuRoC dataset.

In the application scenario you can use COLMAP to build SfM maps (using SuperPoint and NetVLAD). The use of a priori maps can give you more accurate positioning results and no accumulative errors.

Dependencies

• Ubuntu and ROS - noetic/Installation/Ubuntu - ROS Wiki This will help you to install a series of dependencies such as OpenCV.

• OpenVINS - https://docs.openvins.com/gs-installing.html

• Ceres Solver - https://github.com/ceres-solver/ceres-solver

• PyTorch and libtorch - https://pytorch.org/get-started/locally/

  For libtorch, all you need to do is unzip it and fill the file path into loop_hloc/CMakeLists.txt line 22.

  # set your own libtorch path
  set(TORCH_PATH */libtorch/share/cmake/Torch)

Installation Commands

# setup our workspace
mkdir -p ~/workspace/catkin_ws_ov/src/
cd ~/workspace/catkin_ws_ov/src/
# repositories to clone
git clone https://github.com/rpng/open_vins.git
git clone https://github.com/Li-Jesse-Jiaze/ov_hloc.git
# go back to root and build
cd ~/workspace/catkin_ws_ov
catkin build

Download and Convert HF-Net

Download SuperGluePretrainedNetwork/models/weights and place them in support_files/Networks/weights.

cd ~/workspace/catkin_ws_ov/src/ov_hloc/support_files/
python convert_model.py

Run Example

roslaunch ov_msckf subscribe.launch config:=euroc_mav # term 1
rosrun loop_hloc loop_hloc_node ~/workspace/catkin_ws_ov/src/ov_secondary/config/master_config.yaml # term 2
rviz # term 3
rosbag play your/dataset/path/V1_01_easy.bag # term 4

select config/vins_rviz_config.rviz as config in rviz

The pose graph was not very smooth when I tested it on my laptop(RTX 2060 Max-Q 65W). This is mainly because my NetVLAD is using a VGG16 (it tooks more than 50ms for each frame 😠). It would be better to use a lighter backbone (e.g. MobileNetV3) and fine-tuning NetVLAD for your application scenario.

Here's a simple video of it working with VINS-Fusion.