FLVIS

Feedback Loop Based Visual Inertial SLAM

Original repository

This repository is a modified version of FLVIS (see original README below). We facilitate the installation process and the use of Docker.

Docker support

In order to facilitate the installation process, the system is wrapped up using Docker. We provide scripts to create a Docker image, build the system and run it in a Docker container.

Dependencies

• Docker
• ROS
• pose_listener (if you use run_rosario_sequence.sh, see below)

Building the system

Run:

./run.sh -b

This command creates a Docker image, installs all the dependencies and builds the system. The resulting image contains a version of the system ready to be run.

Running the system in VIS mode

If you are not interested in making changes in the source code, you should run the system in VIS mode. Run:

./run.sh -v

The system is launched in a Docker container based on the previously built image. By default, this command executes a launch file which is configured to run the Rosario dataset. If you want to run your own dataset, write a launch file and placed it in the launch/ folder. Then, run the script with the option -l <LAUNCH_FILE_NAME>. For example, if you are testing EuRoC, write euroc_dataset.launch, move it into launch/ and type:

./run.sh -v -l euroc_dataset.launch

Making changes in launch files in the host is possible because launch/ folder is mounted into the Docker container. It is not necessary to access the container through a bash shell to modify these files.

See below for information about input data and visualization.

Running the system in DEV mode

DEV mode allows developers to make changes in the source code, recompile the system and run it with the modifications. To do this, the whole repository is mounted in a container. Run:

./run.sh -d

This opens a bash shell in a docker container. You can edit source files in the host and after that you can use this shell to recompile the system. When the compilation process finishes, you can run the method using roslaunch.

See below for information about input data and visualization.

Input data and visualization

At this point, the system is waiting for input data. Either you can run rosbag play or you can use run_rosario_sequence.sh. If you choose the latter, open a second terminal and run:

./run_rosario_sequence.sh -o <OUTPUT_TRAJECTORY_FILE> <ROSBAG_FILE>

In contrast to what run.sh does, run_rosario_sequence.sh executes commands in the host (you can modify it to use a Docker container).

ROSBAG_FILE is played using rosbag. Also, make sure you have cloned and built pose_listener in your catkin workspace. Default path for the workspace is ${HOME}/catkin_ws, set CATKIN_WS_DIR if the workspace is somewhere else (e.g.: export CATKIN_WS_DIR=$HOME/foo_catkin_ws). pose_listener saves the estimated trajectory in <OUTPUT_TRAJECTORY_FILE> (use absolute path). You can edit run_rosario_sequence.sh if you prefer to save the trajectory using your own methods. Additionally, run_rosario_sequence.sh launches rviz to display visual information during the execution of the system.

Alternatively, if you are not interested in development but in testing or visualization, instead of running run.sh and run_rosario_sequence.sh in two different terminals, you can just run:

./run_rosario_sequence.sh -r -o <OUTPUT_TRAJECTORY_FILE> <ROSBAG_FILE>

This launches a Docker container and executes the default launch file (see LAUNCH_FILE in run.sh). After that, the bagfile is played and rviz and pose_listener are launched. Add -b if you want to turn off the visualization.

Original README

1-Video

EuRoC DataSet MH_05	Handheld Test in Lab	FlVIS on UAV Platform

2-Relevent Publication:

Under Review, a pre-print version can be found here

3-Support Hardware/Dataset:

Intel RealSense D435i Camera
EuRoC MAV Dataset

4-Build The Project

We have tested in the following environment:
Ubuntu 16.04 + ROS Kinetic
Ubuntu 18.04 + ROS melodic
Clone the repository to the catkin work space eg. /catkin_ws/src

git clone https://github.com/Ttoto/FLVIS.git

Install 3rd Part library

cd catkin_ws/src/FLVIS/3rdPartLib/
./install3rdPartLib.sh

Compile

cd ~/catkin_ws
catkin_make

5-Verification

5.1 D435i Camera Depth Mode

5.1.1 Use our recorded rosbag

Download the dataset Link-melab_sn943222072828.bag to /bag folder
Decompress the rosbag:

rosbag decompress melab_sn943222072828.bag

run the following launch files:

roslaunch flvis rviz.launch
roslaunch flvis flvis_bag.launch

5.1.2 Use your own camera:

Install the realsense driver and its ros wrapper
Boot the d435i camera and echo the camera infomation

roslaunch flvis d435i_depth.launch
rostopic echo /camera/infra1/camera_info

You will get the camera infomation like: As shown, where the resolution is 640x480 and fx=384.16455078125; fy=384.16455078125; cx=320.2144470214844;cy=238.94403076171875.
Edit these information in the config yaml file (say: /launch/d435i/sn943222072828_depth.yaml):

image_width: 640
image_height: 480
cam0_intrinsics: [384.16455078125, 384.16455078125, 320.2144470214844, 238.94403076171875]#fx fy cx cy
cam0_distortion_coeffs: [0.0, 0.0, 0.0, 0.0]#k1 k2 r1 r2

In the launch file "flvis_d435i.launch", make sure "/yamlconfigfile" is point to the edited config file

<param name="/yamlconfigfile" type="string" value="$(find flvis)/launch/d435i/sn943222072828_depth.yaml"/>

run the following launch files:

roslaunch flvis rviz.launch
roslaunch flvis flvis_d435i_depth.launch

5.2 D435i Camera Stero Mode

Like what we did in 5.1.2, we need to config the sn943222072828_stereo.yaml
Note that, by default the two camera share the same intrinsic parameters, and the baseline length is 0.05m:

cam0_intrinsics: [384.16455078125, 384.16455078125, 320.2144470214844, 238.94403076171875]#fx fy cx cy
cam0_distortion_coeffs: [0.0, 0.0, 0.0, 0.0]#k1 k2 r1 r2
cam1_intrinsics: [384.16455078125, 384.16455078125, 320.2144470214844, 238.94403076171875]#fx fy cx cy
cam1_distortion_coeffs: [0.0, 0.0, 0.0, 0.0]#k1 k2 r1 r2
T_cam0_cam1:
[ 1.0,  0.0,  0.0,  0.05,
  0.0,  1.0,  0.0,  0.0,
  0.0,  0.0,  1.0,  0.0,
  0.0,  0.0,  0.0,  1.0]

5.3 EuRoC MAV Dataset

Download the dataset(say MH_05_difficult) into the bag folder:

roscd flvis/bag/
wget http://robotics.ethz.ch/~asl-datasets/ijrr_euroc_mav_dataset/machine_hall/MH_05_difficult/MH_05_difficult.bag

Edit the corresponding bag name in flvis_euroc_mav.launch file:

<node pkg="rosbag" type="play" name="rosbag" args="$(find flvis)/bag/MH_05_difficult.bag"/>

run the following launch files:

roslaunch flvis rviz.launch
roslaunch flvis flvis_euroc_mav.launch

Maintainer:

Shengyang Chen(Dept.ME,PolyU): shengyang.chen@connect.polyu.hk
Yajing Zou(Dept.LSGI,PolyU):rick.zou@connect.polyu.hk