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ORB-SLAM2

ORB-SLAM2 Authors: Raul Mur-Artal, Juan D. Tardos, J. M. M. Montiel and Dorian Galvez-Lopez (DBoW2). The original implementation can be found here.

ORB-SLAM2 ROS node

This is the ROS implementation of the ORB-SLAM2 real-time SLAM library for Monocular, Stereo and RGB-D cameras that computes the camera trajectory and a sparse 3D reconstruction (in the stereo and RGB-D case with true scale). It is able to detect loops and relocalize the camera in real time. This implementation removes the Pangolin dependency, and the original viewer. All data I/O is handled via ROS topics. For visualization you can use RViz. This repository is maintained by Lennart Haller on behalf of appliedAI.

Features

• Full ROS compatibility
• Supports a lot of cameras out of the box, such as the Intel RealSense family. See the run section for a list
• Data I/O via ROS topics
• Parameters can be set with the rqt_reconfigure gui during runtime
• Very quick startup through considerably sped up vocab file loading
• Full Map save and load functionality based on this PR.
• Loading of all parameters via launch file
• Supports loading cam parameters from cam_info topic

Related Publications:

[Monocular] Raúl Mur-Artal, J. M. M. Montiel and Juan D. Tardós. ORB-SLAM: A Versatile and Accurate Monocular SLAM System. IEEE Transactions on Robotics, vol. 31, no. 5, pp. 1147-1163, 2015. (2015 IEEE Transactions on Robotics Best Paper Award). PDF.

[Stereo and RGB-D] Raúl Mur-Artal and Juan D. Tardós. ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras. IEEE Transactions on Robotics, vol. 33, no. 5, pp. 1255-1262, 2017. PDF.

[DBoW2 Place Recognizer] Dorian Gálvez-López and Juan D. Tardós. Bags of Binary Words for Fast Place Recognition in Image Sequences. IEEE Transactions on Robotics, vol. 28, no. 5, pp. 1188-1197, 2012. PDF

1. License

ORB-SLAM2 is released under a GPLv3 license. For a list of all code/library dependencies (and associated licenses), please see Dependencies.md.

For a closed-source version of ORB-SLAM2 for commercial purposes, please contact the authors: orbslam (at) unizar (dot) es.

If you use ORB-SLAM2 (Monocular) in an academic work, please cite:

@article{murTRO2015,
  title={{ORB-SLAM}: a Versatile and Accurate Monocular {SLAM} System},
  author={Mur-Artal, Ra\'ul, Montiel, J. M. M. and Tard\'os, Juan D.},
  journal={IEEE Transactions on Robotics},
  volume={31},
  number={5},
  pages={1147--1163},
  doi = {10.1109/TRO.2015.2463671},
  year={2015}
 }

if you use ORB-SLAM2 (Stereo or RGB-D) in an academic work, please cite:

@article{murORB2,
  title={{ORB-SLAM2}: an Open-Source {SLAM} System for Monocular, Stereo and {RGB-D} Cameras},
  author={Mur-Artal, Ra\'ul and Tard\'os, Juan D.},
  journal={IEEE Transactions on Robotics},
  volume={33},
  number={5},
  pages={1255--1262},
  doi = {10.1109/TRO.2017.2705103},
  year={2017}
 }

2. Building orb_slam2_ros

We have tested the library in Ubuntu 16.04 with ROS Kinetic and Ubuntu 18.04 with ROS Melodic. A powerful computer (e.g. i7) will ensure real-time performance and provide more stable and accurate results. A C++11 compiler is needed.

Getting the code

Clone the repository into your catkin workspace:

git clone https://github.com/appliedAI-Initiative/orb_slam_2_ros.git

ROS

This ROS node requires catkin_make_isolated or catkin build to build. This package depends on a number of other ROS packages which ship with the default installation of ROS. If they are not installed use rosdep to install them. In your catkin folder run

sudo rosdep init
rosdep update
rosdep install --from-paths src --ignore-src -r -y

to install all dependencies for all packages. If you already initialized rosdep you get a warning which you can ignore.

Eigen3

Required by g2o. Download and install instructions can be found here. Otherwise Eigen can be installed as a binary with:

sudo apt install libeigen3-dev

Required at least Eigen 3.1.0.

Building

To build the node run

catkin build

in your catkin folder.

3. Configuration

Vocab file

To run the algorithm expects both a vocabulary file (see the paper) which ships with this repository.

Config

The config files for camera calibration and tracking hyper paramters from the original implementation are replaced with ros paramters which get set from a launch file.

ROS parameters, topics and services

Parameters

There are three types of parameters right now: static- and dynamic ros parameters and camera settings. The static parameters are send to the ROS parameter server at startup and are not supposed to change. They are set in the launch files which are located at ros/launch. The parameters are:

• load_map: Bool. If set to true, the node will try to load the map provided with map_file at startup.
• map_file: String. The name of the file the map is loaded from.
• voc_file: String. The location of config vocanulary file mentioned above.
• publish_pointcloud: Bool. If the pointcloud containing all key points (the map) should be published.
• publish_pose: Bool. If a PoseStamped message should be published. Even if this is false the tf will still be published.
• pointcloud_frame_id: String. The Frame id of the Pointcloud/map.
• camera_frame_id: String. The Frame id of the camera position.
• target_frame_id: String. Map transform and pose estimate will be provided in this frame id if set.
• load_calibration_from_cam: Bool. If true, camera calibration is read from a camera_info topic. Otherwise it is read from launch file params.

Dynamic parameters can be changed at runtime. Either by updating them directly via the command line or by using rqt_reconfigure which is the recommended way. The parameters are:

• localize_only: Bool. Toggle from/to only localization. The SLAM will then no longer add no new points to the map.
• reset_map: Bool. Set to true to erase the map and start new. After reset the parameter will automatically update back to false.
• min_num_kf_in_map: Int. Number of key frames a map has to have to not get reset after tracking is lost.
• min_observations_for_ros_map: Int. Number of minimal observations a key point must have to be published in the point cloud. This doesn't influence the behavior of the SLAM itself at all.

Finally, the intrinsic camera calibration parameters along with some hyperparameters can be found in the specific yaml files in orb_slam2/config.

Published topics

The following topics are being published and subscribed to by the nodes:

• All nodes publish (given the settings) a PointCloud2 containing all key points of the map.
• Also all nodes publish (given the settings) a PoseStamped with the current pose of the camera frame, or the target frame if target_frame_id is set.
• Live image from the camera containing the currently found key points and a status text.
• A tf from the pointcloud frame id to the camera frame id (the position), or the target frame if target_frame_id is set.

Subscribed topics

• The mono node subscribes to:

  • /camera/image_raw for the input image
  • /camera/camera_info for camera calibration (if load_calibration_from_cam) is true

• The RGBD node subscribes to:

  • /camera/rgb/image_raw for the RGB image
  • /camera/depth_registered/image_raw for the depth information
  • /camera/rgb/camera_info for camera calibration (if load_calibration_from_cam) is true

• The stereo node subscribes to:

  • image_left/image_color_rect and
  • image_right/image_color_rect for corresponding images
  • image_left/camera_info for camera calibration (if load_calibration_from_cam) is true

4. Services

All nodes offer the possibility to save the map via the service node_type/save_map. So the save_map services are:

• /orb_slam2_rgbd/save_map
• /orb_slam2_mono/save_map
• /orb_slam2_stereo/save_map

The save_map service expects the name of the file the map should be saved at as input.

At the moment, while the save to file takes place, the SLAM is inactive.

5. Run

After sourcing your setup bash using

source devel/setup.bash

Suported cameras

Use the command from the corresponding cell for your camera to launch orb_slam2_ros with the right parameters for your setup.

6. Docker

An easy way is to use orb_slam2_ros with Docker. This repository ships with a Dockerfile based on ROS kinetic. The container includes orb_slam2_ros as well as the Intel RealSense package for quick testing and data collection.

7. FAQ

Here are some answers to frequently asked questions.

How to save the map

To save the map with a simple command line command run one the commands (matching to your node running):

rosservice call /orb_slam2_rgbd/save_map map.bin
rosservice call /orb_slam2_stereo/save_map map.bin
rosservice call /orb_slam2_mono/save_map map.bin

You can replace "map.bin" with any file name you want. The file will be saved at ROS_HOME which is by default ~/.ros

Note that you need to source your catkin workspace in your terminal in order for the services to become available.

Using a new / different camera

You can use this SLAM with almost any mono, stereo or RGBD cam you want. In order to use this with a different camera you need to supply a set of paramters to the algorithm. They are loaded from a launch file from the ros/launch folder.

1. You need the camera intrinsics and some configurations. Here you can read about what the camera calibration parameters mean. Use this ros node to obtain them for your camera. If you use a stereo or RGBD cam in addition to the calibration and resolution you also need to adjust three other parameters: Camera.bf, ThDepth and DepthMapFactor.
2. The ros launch file which is at ros/launch needs to have the correct topics to subscribe to from the new camera. NOTE If your camera supports this, orb_slam_2_ros can subscribe to the camera_info topic and read the camera calibration parameters from there.

Problem running the realsense node

The node for the RealSense fails to launch when running

roslaunch realsense2_camera rs_rgbd.launch

to get the depth stream. Solution: install the rgbd-launch package with the command (make sure to adjust the ROS distro if needed):

sudo apt install ros-melodic-rgbd-launch