在LARVIO基础上融合GPS，可以参考 https://zhuanlan.zhihu.com/p/532875991 https://zhuanlan.zhihu.com/p/142714515
相关论文
- Lightweight hybrid Visual-Inertial Odometry with Closed-Form Zero Velocity Update
cd ~/LARVIO_GPS/ros_wrapper
catkin_make
source devel/setup.bash
roslaunch larvio android.launch
rosbag play xxx.bag
LARVIO没有融合GPS
LARVIO融合GPS
LARVIO is short for Lightweight, Accurate and Robust monocular Visual Inertial Odometry, which is based on hybrid EKF VIO. It is featured by augmenting features with long track length into the filter state of MSCKF by 1D IDP to provide accurate positioning results.
The core algorithm of LARVIO depends on Eigen, Boost, Suitesparse, Ceres and OpenCV, making the algorithm of good portability.
A single-thread toyish example as well as a ROS nodelet package for LARVIO is provided in this repo.
Notice that Hamilton quaternion is utilized in LARVIO, which is a little bit different from the JPL quaternion used in traditional MSCKF community. The filter formulation is thus derivated from scratch. Please check our CJA2020 and Senors2019 papers for details.
This is the results of an earlier version of LARVIO. Due to the changes, the current repo might not reproduce the exact results as below.
RMSE on EuRoC dataset are listed.
Evaluations below are done using PetWorm/sim3_evaluate_tool.
In the newest update, online imu-cam extrinsic and timestamp error calibration in VINS-MONO are turned on to explore its extreme ability. While in this setup, the V102 sequence would somehow fail. The result of VINS-MONO in V102 below is of setup without online calibration.
Results of our algorithm are repeatible in every run of every computer I tested so far.
This package has been successfully deployed on ARM (Jetson Nano and Jetson TX2, realtime without GPU refinement). The performances are comparable to the results on PCs.
Below is the exper1ment result in our office. A TX2-based multi-thread CPU-only implementation without ROS was developed here. We used MYNT-EYE-D camera SDK to collect monocular images and IMU data, and estimate the camera poses in realtime. We walk around out the office to the corridor or the neighbor room, and return to the start point (in white circle) for a couple of times.
This repo is for academic use only.
LARVIO is originally developed based on MSCKF_VIO. Tremendous changes has been made, including the interface, visualization, visual front-end and filter details.
LARVIO also benefits from VINS-MONO and ORB_SLAM2.
We would like to thank the authors of repos above for their great contribution. We kept the copyright announcement of these repos.
LARVIO is an EKF-based monocular VIO. Loop closure was not applied in our algorithm.
A hybrid EKF architecture is utilized, which is based on the work of Mingyang Li. It augments features with long track length into the filter state. In LARVIO, One-Dimensional Inverse Depth Parametrization is utilized to parametrize the augmented feature state, which is different from the original 3d solution by Li. This novelty improves the computational efficiency compare to the 3d solution. The positioning precision is also improved thanks to the utilization of complete constraints of features with long track length.
It is capable of online imu-cam extrinsic calibration, online timestamp error calibration and online imu intrinsic calibration.
LARVIO can be automatically initialized in either static or dynamic scenerios.
We applied a ORB-descriptor assisted optical flow tracking visual front-end to improve the feature tracking performances.
A closed-form ZUPT measurement update is proposed to cope with the static scene.
LARVIO is a feasible software.
Users can change the settings in config file to set the VIO as MSCKF-only, 3d hybrid or 1d hybrid solutions. And all the online calibration functions can be turned on or off in each solution by the config file.
LARVIO depends on Eigen, Boost, Suitesparse, Ceres and OpenCV for the core algorithm.
The toyish example depends on OpenCV (4.1.2 on OSX and 3.4.6 on Ubuntu 16.04/18.04), Pangolin is needed for visualization. Notice that extra gcc 7 installation is needed for Ubuntu 16.04.
The ROS nodelet package has been tested on Kinetic and Melodic for Ubuntu 16.04/18.04. Following ROS packages are needed: tf, cv_bridge, message_filters and image_transport.
This part show how to play LARVIO with EuRoC dataset.
The toyish LARVIO example is a CMake based software. After install the dependencies, try commands below to compile the software:
cd LARVIO
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=Release ..
make
An example is given in LARVIO/run.sh to show how to run the example.
A ROS nodelet package is provided in LARVIO/ros_wrapper. It has been tested on Kinetic and Melodic. Use commands below to compile the nodelet:
cd YOUR_PATH/LARVIO/ros_wrapper
catkin_make
After building it, launch the LARVIO by:
. YOUR_PATH/LARVIO/ros_wrapper/devel/setup.bash
roslaunch larvio larvio_euroc.launch
Open a new terminal, and launch the rviz for visualization by (optional):
. YOUR_PATH/LARVIO/ros_wrapper/devel/setup.bash
roslaunch larvio larvio_rviz.launch
Open a new terminal to play the dataset:
rosbag play MH_01_easy.bag
A Dockerfile is provided in LARVIO/docker. After building it, you need to load dateset and modify the run.sh in container to run toyish example, or use 'roslaunch' to run the ROS package. Also, GUI is needed in the host to display the Pangolin and rviz view.
There is another VNC docker image which is convinent for monitoring the rviz view. Click petworm/vnc-larvio-playground to directly pull this image, or build it from source with PetWorm/docker-larvio-playground.
Please cite our CJA paper if you use LARVIO in your research:
@article{qiu2020lightweight,
title={Lightweight hybrid Visual-Inertial Odometry with Closed-Form Zero Velocity Update},
author={Qiu, Xiaochen and Zhang, Hai and Fu, Wenxing},
journal={Chinese Journal of Aeronautics},
year={2020},
publisher={Elsevier}
}Another earlier work illustrating some parts of LARVIO is as below:
@article{qiu2019monocular,
title={Monocular Visual-Inertial Odometry with an Unbiased Linear System Model and Robust Feature Tracking Front-End},
author={Qiu, Xiaochen and Zhang, Hai and Fu, Wenxing and Zhao, Chenxu and Jin, Yanqiong},
journal={Sensors},
volume={19},
number={8},
pages={1941},
year={2019},
publisher={Multidisciplinary Digital Publishing Institute}
}