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 vizualization you can use RViz. This repository is maintained by Lennart Haller on behalf of appliedAI.

Features

• Full ROS compatibility
• Supports the Intel RealSense R200 and D435 out of the box
• 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

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. Prerequisites

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.

C++11

We use the new thread and chrono functionalities of C++11.

OpenCV

We use OpenCV to manipulate images and features. OpenCV should be installed along with ROS.

Eigen3

Required by g2o (see below). Download and install instructions can be found at: http://eigen.tuxfamily.org. Otherwise Eigen can be installed as a binary with:

sudo apt install libeigen3-dev

Required at least 3.1.0.

DBoW2 and g2o (Included in orb_slam2/Thirdparty)

We use modified versions of the DBoW2 library to perform place recognition and g2o library to perform non-linear optimizations. Both modified libraries (which are BSD) are included in the Thirdparty folder.

ROS / catkin

This ROS node requires catkin_make_isolated or catkin build to build.

3. Building the ORB-SLAM2 ROS node

Getting the code

Clone the repository into your catkin workspace:

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

Building

To build the node run

catkin build

in your catkin folder.

4. Configuration

Config file

To run the algorithm expects both a vocabulary file (see the paper) and a config file with the camera- and some hyper parameters. The vocab file ships with this repository, together with config files for the Intel RealSense r200 camera. If you want to use any other camera you need to adjust the file (you can use one of the provided ones as a template). They are at orb_slam2/config.

ROS parameters and topics

Parameters

There are three types of parameters right now: static- and dynamic ros parameters and camera settings from the config file. 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:

• publish_pointcloud: Bool. If the pointcloud containing all key points (the map) should be published.
• pointcloud_frame_id: String. The Frame id of the Pointcloud/map.
• camera_frame_id: String. The Frame id of the camera position.

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.
• 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).

Subscribed topics

• The mono node subscribes to /camera/image_raw for the input image.

• The RGBD node subscribes to /camera/rgb/image_raw for the RGB image and

• /camera/depth_registered/image_raw for the depth information.

• The stereo node subscribes to image_left/image_color_rect and

• image_right/image_color_rect for corresponding images.

5. Run

After sourcing your setup bash using

source devel/setup.bash

you can run the the corresponding nodes with one of the following commands:

roslaunch orb_slam2_ros orb_slam2_r200_mono.launch
roslaunch orb_slam2_ros orb_slam2_r200_stereo.launch
roslaunch orb_slam2_ros orb_slam2_r200_rgbd.launch
roslaunch orb_slam2_ros orb_slam2_d435_mono.launch
roslaunch orb_slam2_ros orb_slam2_d435_rgbd.launch

5. FAQ

Here are some answers to frequently asked questions.

Using a new / different camera

You can use this SLAM with almost any mono, stereo or RGBD cam you want. There are two files which need to be adjusted for a new camera:

1. The yaml config file at orb_slam2/config for the camera intrinsics and some configurations. Here you can read about what the 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 need to adjust the other parameters such as 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.

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