At first ,judge if your device type is mynteye-d or mynteye-s,then follow the following installation process: Install with MYNT-EYE-S-SDK / Install with MYNT-EYE-D-SDK.
- Download and install MYNT-EYE-S-SDK.
- Install dependencies and build MYNT-EYE-OKVIS-Sample follow the procedure of the Original OKVIS here.
- Update camera parameters to here.
- run okvis using mynteye camaera.
First install dependencies based on the original OKVIS,and the follow the type:
git clone -b mynteye https://github.com/slightech/MYNT-EYE-OKVIS-Sample.git
cd MYNT-EYE-OKVIS-Sample/
mkdir build && cd build
cmake ..
make
Through the GetIntrinsics() and GetExtrinsics() function of the MYNT-EYE-S-SDK API, you can get the camaera calibration parameters of the currently open device,follow the steps
cd MYNT-EYE-S-SDK
./samples/_output/bin/tutorials/get_img_params
After running the above type, pinhole's distortion_parameters and camera parameters is obtained , and then update to here according to following format. It should be noted that only first four parameters of coeffs need to be filled in the distortion_coefficients.
distortion_coefficients: [coeffs]
focal_length: [fx, fy]
principal_point: [cx, cy]
distortion_type: radialtangential
Go to MYNT-EYE-OKVIS-Sample/build folder and Run the application okvis_app_mynteye_s:
cd MYNT-EYE-OKVIS-Sample/bin
./okvis_app_mynteye_s ../config/config_mynteye_s.yaml
If you would like to run the software/library on your own hardware setup, be aware that good results (or results at all) may only be obtained with appropriate calibration of the
- camera intrinsics,
- camera extrinsics (poses relative to the IMU),
- knowledge about the IMU noise parameters,
- and ACCURATE TIME SYNCHRONISATION OF ALL SENSORS.
To perform a calibration yourself, we recommend the following:
-
Get Kalibr by following the instructions here https://github.com/ethz-asl/kalibr/wiki/installation . If you decide to build from source and you run ROS indigo checkout pull request 3:
git fetch origin pull/3/head:request3 git checkout request3 -
Follow https://github.com/ethz-asl/kalibr/wiki/multiple-camera-calibration to calibrate intrinsic and extrinsic parameters of the cameras. If you receive an error message that the tool was unable to make an initial guess on focal length, make sure that your recorded dataset contains frames that have the whole calibration target in view.
-
Follow https://github.com/ethz-asl/kalibr/wiki/camera-imu-calibration to get estimates for the spatial parameters of the cameras with respect to the IMU.
- Download and install MYNT-EYE-D-SDK.
- Install dependencies and build MYNT-EYE-OKVIS-Sample follow the procedure of the Original OKVIS .
- Update camera parameters to here.
- run okvis using mynteye depth camaera.
First install dependencies based on the original OKVIS,and the follow the type:
git clone -b mynteye https://github.com/slightech/MYNT-EYE-OKVIS-Sample.git
cd MYNT-EYE-OKVIS-Sample/
mkdir build && cd build
cmake ..
make
Through the MYNT-EYE-D-SDK API, you can get the camaera and IMU calibration parameters of the currently open device,follow the steps
cd MYNT-EYE-D-SDK
./samples/_output/bin/get_img_params
./samples/_output/bin/get_imu_params
After running the above type, pinhole's distortion_parameters and camera parameters is obtained , and then update to here according to following format. It should be noted that only first four parameters of coeffs need to be filled in the distortion_coefficients.
distortion_coefficients: [coeffs]
focal_length: [fx, fy]
principal_point: [cx, cy]
distortion_type: radialtangential
Run camera mynteye_wrapper_d
cd MYNT-EYE-D-SDK
source wrappers/ros/devel/setup.bash
roslaunch mynteye_wrapper_d mynteye.launch
Run MYNT-EYE-OKVIS-Sample open another terminal and follow the steps.
cd MYNT-EYE-OKVIS-Sample/build
source devel/setup.bash
roslaunch okvis_ros mynteye_d.launch
And use rviz to display
cd ~/catkin_okvis/src/MYNT-EYE-OKVIS-Sample/config
rosrun rviz rviz -d rviz.rviz
If you would like to run the software/library on your own hardware setup, be aware that good results (or results at all) may only be obtained with appropriate calibration of the
camera intrinsics,
camera extrinsics (poses relative to the IMU),
knowledge about the IMU noise parameters,
and ACCURATE TIME SYNCHRONISATION OF ALL SENSORS.
To perform a calibration yourself, we recommend the following:
Get Kalibr by following the instructions here https://github.com/ethz-asl/kalibr/wiki/installation . If you decide to build from source and you run ROS indigo checkout pull request 3:
git fetch origin pull/3/head:request3
git checkout request3
Follow https://github.com/ethz-asl/kalibr/wiki/multiple-camera-calibration to calibrate intrinsic and extrinsic parameters of the cameras. If you receive an error message that the tool was unable to make an initial guess on focal length, make sure that your recorded dataset contains frames that have the whole calibration target in view.
Follow https://github.com/ethz-asl/kalibr/wiki/camera-imu-calibration to get estimates for the spatial parameters of the cameras with respect to the IMU.
Welcome to OKVIS: Open Keyframe-based Visual-Inertial SLAM.
This is the Author's implementation of the [1] and [3] with more results in [2].
[1] Stefan Leutenegger, Simon Lynen, Michael Bosse, Roland Siegwart and Paul Timothy Furgale. Keyframe-based visual–inertial odometry using nonlinear optimization. The International Journal of Robotics Research, 2015.
[2] Stefan Leutenegger. Unmanned Solar Airplanes: Design and Algorithms for Efficient and Robust Autonomous Operation. Doctoral dissertation, 2014.
[3] Stefan Leutenegger, Paul Timothy Furgale, Vincent Rabaud, Margarita Chli, Kurt Konolige, Roland Siegwart. Keyframe-Based Visual-Inertial SLAM using Nonlinear Optimization. In Proceedings of Robotics: Science and Systems, 2013.
Note that the codebase that you are provided here is free of charge and without any warranty. This is bleeding edge research software.
Also note that the quaternion standard has been adapted to match Eigen/ROS, thus some related mathematical description in [1,2,3] will not match the implementation here.
If you publish work that relates to this software, please cite at least [1].
This is a catkin package that wraps the pure CMake project.
You will need to install the following dependencies,
-
ROS (currently supported: hydro, indigo and jade). Read the instructions in http://wiki.ros.org/indigo/Installation/Ubuntu. You will need the additional package pcl-ros as (assuming indigo)
sudo apt-get install ros-indigo-pcl-ros -
google-glog + gflags,
sudo apt-get install libgoogle-glog-dev -
The following should get installed through ROS anyway:
sudo apt-get install libatlas-base-dev libeigen3-dev libsuitesparse-dev sudo apt-get install libopencv-dev libboost-dev libboost-filesystem-dev -
Optional: use the the package with the Skybotix VI sensor. Note that this requires a system install, not just as ROS package. Also note that Skybotix OSX support is experimental (checkout the feature/osx branch).
git clone https://github.com/ethz-asl/libvisensor.git cd libvisensor ./install_libvisensor.sh
then download and expand the archive into your catkin workspace:
wget https://www.doc.ic.ac.uk/~sleutene/software/okvis_ros-1.1.3.zip
unzip okvis_ros-1.1.3.zip && rm okvis_ros-1.1.3.zip
Or, clone the repository from github into your catkin workspace:
git clone --recursive [email protected]:ethz-asl/okvis_ros.git
or
git clone --recursive https://github.com/ethz-asl/okvis_ros.git
From the catkin workspace root, type
catkin_make
You will find a demo application in okvis_apps. It can process datasets in the ASL/ETH format.
In order to run a minimal working example, follow the steps below:
-
Download a dataset of your choice from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets. Assuming you downloaded MH_01_easy/. You will find a corresponding calibration / estimator configuration in the okvis/config folder.
-
Run the app as
./okvis_apps path/to/okvis_ros/okvis/config/config_fpga_p2_euroc.yaml path/to/MH_01_easy/
You can also run a dataset processing ros node that will publish topics that can be visualized with rviz
rosrun okvis_ros okvis_node_synchronous path/to/okvis_ros/okvis/config/config_fpga_p2_euroc.yaml path/to/MH_01_easy/
Use the rviz.rviz configuration in the okvis_ros/config/ directory to get the pose / landmark display.
If you want to run the live application connecting to a sensor, use the okvis_node application (modify the launch file launch/okvis_node.launch).
In terms of coordinate frames and notation,
- W denotes the OKVIS World frame (z up),
- C_i denotes the i-th camera frame,
- S denotes the IMU sensor frame,
- B denotes a (user-specified) body frame.
The output of the okvis library is the pose T_WS as a position r_WS and quaternion q_WS, followed by the velocity in World frame v_W and gyro biases (b_g) as well as accelerometer biases (b_a). See the example application to get an idea on how to use the estimator and its outputs (callbacks returning states).
The okvis_node ROS application will publish a configurable state -- see just below.
The okvis/config folder contains example configuration files. Please read the documentation of the individual parameters in the yaml file carefully. You have various options to trade-off accuracy and computational expense as well as to enable online calibration. You also have various options concerning the things that will get published -- in particular weather or not landmarks should be published (may be important to turn off for on-bard operation). Moreover, you can specify how the body frame is specified (T_BS) or define a custom World frame. In other words, the final pose published will be T_Wc_B = T_Wc_W * T_WS * T_BS^(-1) . You have the option to express the velocity as well as the rotation rates in either B, S, or Wc.
If you would like to run the software/library on your own hardware setup, be aware that good results (or results at all) may only be obtained with appropriate calibration of the
- camera intrinsics,
- camera extrinsics (poses relative to the IMU),
- knowledge about the IMU noise parameters,
- and ACCURATE TIME SYNCHRONISATION OF ALL SENSORS.
To perform a calibration yourself, we recommend the following:
-
Get Kalibr by following the instructions here https://github.com/ethz-asl/kalibr/wiki/installation . If you decide to build from source and you run ROS indigo checkout pull request 3:
git fetch origin pull/3/head:request3 git checkout request3
-
Follow https://github.com/ethz-asl/kalibr/wiki/multiple-camera-calibration to calibrate intrinsic and extrinsic parameters of the cameras. If you receive an error message that the tool was unable to make an initial guess on focal length, make sure that your recorded dataset contains frames that have the whole calibration target in view.
-
Follow https://github.com/ethz-asl/kalibr/wiki/camera-imu-calibration to get estimates for the spatial parameters of the cameras with respect to the IMU.
-
Contact [email protected] to request access to the bitbucket repository.
-
Programming guidelines: please follow https://github.com/ethz-asl/programming_guidelines/wiki/Cpp-Coding-Style-Guidelines .
-
Writing tests: please write unit tests (gtest).
-
Code review: please create a pull request for all changes proposed. The pull request will be reviewed by an admin before merging.
The developpers will be happy to assist you or to consider bug reports / feature requests. But questions that can be answered reading this document will be ignored. Please contact [email protected].
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