Snapshot of ethz-asl/okvis: 1.4k★, C++. OKVIS: Open Keyframe-based Visual-Inertial SLAM.
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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].
License
The 3-clause BSD license (see file LICENSE) applies.
How do I get set up?
This is a pure cmake project.
You will need to install the following dependencies,
OpenCV 2.4-3.0: follow the instructions on http://opencv.org/ or install
via
sudo apt-get install libopencv-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
To change the cmake build type for the whole project use:
mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
make -j8
NOTE: if you want to use the library, install the project (default or somewhere
else), so the dependencies can be resolved.
make install
Running the demo application
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
config folder.
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).
Configuration files
The 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.
HEALTH WARNING: calibration
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:
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.
Using the library
Here's a minimal example of your CMakeLists.txt to build a project using
OKVIS.
cmake_minimum_required(VERSION 2.8)
set(OKVIS_INSTALLATION <path/to/install>) # point to installation
# require OpenCV
find_package( OpenCV COMPONENTS core highgui imgproc features2d REQUIRED )
include_directories(BEFORE ${OpenCV_INCLUDE_DIRS})
# require okvis
find_package( okvis 1.1 REQUIRED)
include_directories(${OKVIS_INCLUDE_DIRS})
# require brisk
find_package( brisk 2 REQUIRED)
include_directories(${BRISK_INCLUDE_DIRS})
# require ceres
list(APPEND CMAKE_PREFIX_PATH ${OKVIS_INSTALLATION})
find_package( Ceres REQUIRED )
include_directories(${CERES_INCLUDE_DIRS})
# require OpenGV
find_package(opengv REQUIRED)
# VISensor, if available
list(APPEND CMAKE_MODULE_PATH ${OKVIS_INSTALLATION}/lib/CMake)
find_package(VISensor)
if(VISENSORDRIVER_FOUND)
message(STATUS "Found libvisensor.")
else()
message(STATUS "libvisensor not found")
endif()
# now continue with your project-specific stuff...
Contribution guidelines
Contact [email protected] to request access to the bitbucket
repository.
Code review: please create a pull request for all changes proposed. The pull
request will be reviewed by an admin before merging.
Support
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].
The most recent commit recorded on ethz-asl/okvis was 5.0 years ago, based on the GitHub push timestamp. The repository has 541 forks — one of the better signals of community interest.
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What is ethz-asl/okvis?
ethz-asl/okvis (ethz-asl/okvis) is a C++ project on GitHub. From the project's own README: OKVIS: Open Keyframe-based Visual-Inertial SLAM.
What language is ethz-asl/okvis written in?
ethz-asl/okvis is written primarily in C++. GitHub's language field is based on the largest share of bytes in the default branch.
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