Ensemble Visual-Inertial Odometry (EnVIO)

Authors : Jae Hyung Jung, Yeongkwon Choe, and Chan Gook Park

1. Overview

This is a ROS package of Ensemble Visual-Inertial Odometry (EnVIO) written in C++. It features a photometric (direct) measurement model and stochastic linearization that are implemented by iterated extended Kalman filter fully built on the matrix Lie group. EnVIO takes time-synced stereo images and IMU readings as input and outputs the current vehicle pose and feature depths at the current camera frame with their estimated uncertainties.

2. Build

This package was tested on Ubuntu 16.04 (ROS Kinetic) with Eigen 3.3.7 for matrix computation and OpenCV 3.3.1-dev for image processing in C++11.
There are no additional dependencies, we hope this package can be built without any difficulties in different environments.
We use the catkin build system :

cd catkin_ws
catkin_make

3. Run (EuRoC example)

Configuration and launch files are prepared in config/euroc/camchain-imucam-euroc.yaml and launch/nesl_envio_euroc.launch.
The configuration files are output by Kalibr toolbox.
Filter and image processing parameters are set from the launch file.
Please note that our filter implementation requires static state at the beginning to initialize tilt angles, velocity and gyroscope biases. The temporal window for this process can be set by num_init_samples in the launch file.
As default our package outputs est_out.txt that includes estimated states.

roslaunch ensemble_vio nesl_envio_euroc.launch
roslaunch ensemble_vio nesl_envio_rviz.launch
rosbag play rosbag.bag

4. Hand-held dataset with textureless wall

We recorded stereo images & IMU readings, and high precision position reference by using MYNTEYE S1030 and Leica TS-16, respectively in BLDG. 301 at our campus, SNU. It features the textureless white wall during the course.

ex_screenshot

Trajectory information is summarized as below

Sequence num. Length [m] Duration [sec] White wall ?

snu301_00 89 105 X

snu301_01 117 143 O

snu301_02 147 182 O

snu301_03 159 199 O
You can download our dataset here. (6.2 GB)

Sequence num.	Length [m]	Duration [sec]	White wall ?
snu301_00	89	105	X
snu301_01	117	143	O
snu301_02	147	182	O
snu301_03	159	199	O

Please use nesl_envio_mynt.launch to test the dataset :

roslaunch ensemble_vio nesl_envio_mynt.launch
roslaunch ensemble_vio nesl_envio_rviz.launch
rosbag play snu301_xx.bag

5. Run your own device

Our implementation assumes that stereo camera is hardware-synced and the spatio-temporal parameters for cameras and IMU are calibrated as it is a critical step in sensor fusion.
You can calibrate your visual-inertial sensor using Kalibr toolbox and place the output file in config.
The input ROS topics and filter parameters are set in launch.
With low cost IMUs as in EuRoC sensor suite, you can use the default parameters of EuRoC example file.

6. Citation

If you feel this work helpful to your academic research, we kindly ask you to cite our paper :

@article{EnVIO_TRO,
  title={Photometric Visual-Inertial Navigation with Uncertainty-Aware Ensembles},
  author={Jung, Jae Hyung and Choe, Yeongkwon and Park, Chan Gook},
  journal={IEEE Transactions on Robotics},
  year={2022},
  publisher={IEEE}
}

7. Acknowledgements

This research was supported in part by Unmanned Vehicle Advanced Research Center funded by the Ministry of Science and ICT, the Republic of Korea and in part by Hyundai NGV Company.

8. License

Our source code is released under GPLv3 license. If there are any issues in our source code please contact to the author (lastflowers@snu.ac.kr).

lastflowers / envio

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