The `MSCKF_VIO` package is a stereo version of MSCKF. The software takes in synchronized stereo images and IMU messages and generates real-time 6DOF pose estimation of the IMU frame.
Penn Software License. See LICENSE.txt for further details.
## Dependencies
Most of the dependencies are standard including `Eigen`, `OpenCV`, and `Boost`. The standard shipment from Ubuntu 16.04 and ROS Kinetic works fine. One special requirement is `suitesparse`, which can be installed through,
The software is a standard catkin package. Make sure the package is on `ROS_PACKAGE_PATH` after cloning the package to your workspace. And the normal procedure for compiling a catkin package should work.
An accurate calibration is crucial for successfully running the software. To get the best performance of the software, the stereo cameras and IMU should be hardware synchronized. Note that for the stereo calibration, which includes the camera intrinsics, distortion, and extrinsics between the two cameras, you have to use a calibration software. **Manually setting these parameters will not be accurate enough.** [Kalibr](https://github.com/ethz-asl/kalibr) can be used for the stereo calibration and also to get the transformation between the stereo cameras and IMU. The yaml file generated by Kalibr can be directly used in this software. See calibration files in the `config` folder for details. The two calibration files in the `config` folder should work directly with the EuRoC and [fast flight](https://github.com/KumarRobotics/msckf_vio/wiki) datasets. The convention of the calibration file is as follows:
The filter uses the first 200 IMU messages to initialize the gyro bias, acc bias, and initial orientation. Therefore, the robot is required to start from a stationary state in order to initialize the VIO successfully.
## EuRoC and UPenn Fast flight dataset example usage
Once the `msckf_vio` is built and sourced (via `source <path to catkin_ws>/devel/setup.bash`), there are two launch files prepared for the [EuRoC](https://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets) and [UPenn fast flight](https://github.com/KumarRobotics/msckf_vio/wiki/Dataset) dataset with launch files `msckf_vio_euroc.launch` and `msckf_vio_fla.launch` respectively. The launch files instanciates two ROS nodes:
*`image_processor` processes stereo images to detect and track features
*`vio` obtains feature measurements from the `image_processor` and tightly fuses them with the IMU messages to estimate pose.
These launch files can be executed via
```
roslaunch msckf_vio msckf_vio_euroc.launch
or
roslaunch msckf_vio msckf_vio_fla.launch
```
Once the nodes are running you need to run the dataset rosbags, for example:
```
rosbag play V1_01_easy.bag
```
As mentioned in the previous section **The robot is required to start from a stationary state in order to initialize the VIO successfully**
To visualize the pose and feature estimations you can use the provided rviz configurations found in `msckf_vio/rviz` folder to visualize corresponding datatset estimations (EuRoC: `rviz_euroc_config.rviz`, Fast dataset: `rvis_fla_config.rviz`).
