srrg_proslam

srrg_proslam

Graph SLAM from a programmer’s perspective

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ProSLAM: Programmers SLAM

Contributors: Dominik Schlegel, Mirco Colosi, Giorgio Grisetti
As this is a working repository, none of the code is assumed to be static.
For related publications please refer to revision 69671dfe


Demo videos

ProSLAM: Full run KITTI Sequence 00 updated (real-time, 1 thread@2.40GHz/i7-4700MQ)
ProSLAM: Full run KITTI Sequence 00 (real-time, 1 thread@2.40GHz/i7-4700MQ)
ProSLAM: Full run KITTI Sequence 01 (real-time, 1 thread@2.40GHz/i7-4700MQ)
ProSLAM: Full run KITTI Sequence 06 (real-time, 1 thread@3.50GHz/i7-4770K)
ProSLAM: Full run KITTI Sequence 10 (real-time, 1 thread@2.40GHz/i7-4700MQ)
ProSLAM: Full run EuRoC MH_01_easy (real-time, 1 thread@3.50GHz/i7-4770K)

(All of the above are clickable YouTube links)


Supported environments

Currently Linux only:

  • Ubuntu 14.04 LTS + ROS Indigo /(OpenCV2 + Qt4)
  • Ubuntu 16.04 LTS + ROS Kinetic/(OpenCV3 + Qt5)

The complete system runs on a single thread (visualization components are synchronous)


Code statistics | Revision 69671dfe

cloc srrg_proslam/src/
http://cloc.sourceforge.net v 1.60
Language files blank lines comment lines code lines
C++ 18 737 646 2874
C/C++ Header 18 414 303 1086
CMake 8 8 1 75
SUM: 44 1159 950 4035

How do I get set up?

1) install the Ubuntu packages

sudo apt-get install build-essential libeigen3-dev libsuitesparse-dev freeglut3-dev libqglviewer-dev

2) download and install

or (OpenCV + Qt)


3) download and install the colorful Catkin Command Line Tools: https://catkin-tools.readthedocs.io/en/latest/installing.html (alternatively one can also use ROS catkin):

sudo apt-get install python-catkin-tools

4) set the environment variable $G2O_ROOT to use your own g2o installation - or clone g2o for catkin (https://github.com/yorsh87/g2o_catkin) to your catkin workspace:

sudo apt-get install ninja-build
git clone https://github.com/yorsh87/g2o_catkin.git

and build it (slow as it will perform a download using unladen swallows):

catkin build g2o_catkin

5) download this repository to your catkin workspace:

git clone https://gitlab.com/srrg-software/srrg_proslam.git

enter the project directory in your catkin workspace (e.g. ../src/srrg_proslam) and fetch the modular SRRG libraries by executing the script:

./pull_srrg_packages.bash

then build the project using:

catkin build srrg_proslam

CMake variables that must be set when building without ROS or to select specific libraries:

-D OpenCV_DIR=/your/path/to/the/opencv/build/folder
-D G2O_ROOT=/your/path/to/the/g2o/root/folder

How do I check if it works?

1) download the KITTI Sequence 00 into a folder on your computer: https://drive.google.com/open?id=0ByaBRAPfmgEqdXhJRmktQ2lsMEE (2.8GB)


2) launch a terminal in that folder and uncompress the tarball:

tar -xzvf 00.tar.gz

The folder should now contain 4 files (.txt) and 1 folder (.txt.d) plus the tarball 00.tar.gz


3) run the system directly in the folder (rosrun is used for convenience only, the binary can also be launched normally with ./srrg_proslam_app):

rosrun srrg_proslam srrg_proslam_app 00.txt -use-gui -show-top

Three windows will pop up - "input: images", "output: map (bird view)" and "output: map (top view)"


4) press [Backspace] on the input window to toggle between automatic processing and stepwise (press [Space] for stepping) mode


5) press [H] to view the available commands for the output windows (Number keys 1-8)


6) press [Esc] to terminate the system prematurely` (in an input or output window)


7) to see the raw system performance simply launch srrg_proslam without any parameters other than the input dataset:

rosrun srrg_proslam srrg_proslam_app 00.txt

After a complete run we can evaluate the KITTI error statistics by calling:

rosrun srrg_proslam kitti_evaluate_odometry trajectory.txt 00_gt.txt 00.txt

Pre-formatted datasets

Run procedure remains identical to the one above (only the dataset name has to be adjusted, e.g. 00.txt becomes MH_01_easy.txt)
The EuRoC datasets generally require image histogram equalization for best performance (option -equalize-histogram/-eh)


Custom stereo camera sensor input / ROS node

On-the-fly raw stereo image processing with custom stereo camera parameters will be supported shortly.
Please use the provided datasets in SRRG format.

The ROS node (srrg_proslam_node) is currently under development.


Configuration file (YAML)

ProSLAM supports classic YAML configuration files, enabling fine-grained adjustment of deep system parameters.
Example configuration files can be found in the configurations folder.
Upon launch the system scans the working directory for a default configuration file (configuration.yaml) and loads it (if none is present, internal default values apply).
A custom configuration file can be specified as follows:

rosrun srrg_proslam srrg_proslam_app 00.txt -c my_configuration.yaml

It doesn't work?

Feel free to contact the maintainer at any time (see package.xml)