A ROI-mask Feature Tracker for Monocular Visual-Inertial System, ICARCE 2022.
1.1 Ubuntu and ROS Ubuntu 16.04. ROS Kinetic. ROS Installation additional ROS pacakge
sudo apt-get install ros-YOUR_DISTRO-cv-bridge ros-YOUR_DISTRO-tf ros-YOUR_DISTRO-message-filters ros-YOUR_DISTRO-image-transport
1.2 CUDA and pytorch Follow Pytorch Installation.
1.3 Ceres Solver Follow Ceres Installation, remember to sudo make install.
1.4 gRPC and Protocol Buffers Follow gRPC Installation, remember to sudo make install. (Our testing environment: Ubuntu 16.04, ROS Kinetic, OpenCV 3.3.1, Protobuf 3.11.2, gRPC 1.9.0, torch 1.8.2, CUDA 11.1)
Clone the repository and catkin_make:
cd ~/catkin_ws/src
git clone https://github.com/sunjiayuanro/VINS-Mask.git
cd ../
catkin_make
source ~/catkin_ws/devel/setup.bash
Download EuRoC MAV Dataset. We only use one camera.
3.1 Run feature extraction service.
# If you have an Anaconda environment
conda activate YOUR_ENV
# Run feature extraction service
cd ~/catkin_ws/src/VINS-Mask/deep_feature
python feat_service.py
3.2 Launch the vins_estimator, rviz and play the bag file respectively. Take MH_01 for example
roslaunch vins_estimator euroc_no_extrinsic_param.launch
roslaunch vins_estimator vins_rviz.launch
rosbag play YOUR_PATH_TO_DATASET/MH_01_easy.bag
3.3 More helpful tutorials can be found in VIMS-Mono.
@INPROCEEDINGS{vinsmask,
author={Sun, Jiayuan and Song, Fangwei and Ji, Luping},
booktitle={2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)},
title={VINS-Mask: A ROI-mask Feature Tracker for Monocular Visual-inertial System},
year={2022}
}
Thanks for VINS-Mono.