# SLAM

## DiffBot Slam Package

This package contains launch files and configurations for different simultaneous localization and mapping (SLAM) algorithms to map the environment of the robot in 2D, although some of these algorithms can be used to map in 3D.

 1 2 3 4 5 fjp@diffbot:~/catkin_ws/src/diffbot$catkin create pkg diffbot_slam --catkin-deps diffbot_navigation gmapping Creating package "diffbot_slam" in "/home/fjp/git/ros_ws/src/diffbot"... Created file diffbot_slam/package.xml Created file diffbot_slam/CMakeLists.txt Successfully created package files in /home/fjp/git/ros_ws/src/diffbot/diffbot_slam.  Additional runtime dependencies are: cartographer_ros, hector_slam, frontier_exploration and explore_lite. These are added to this workspace using vcstool (TODO). As you can see this package has lots of dependencies to test different slam implementations and frontier exploration approaches. To run this package these dependencies need to be installed and are set as exec_depend in the package.xml. Currently only gmapping provides a ROS Noetic Ubuntu package that can be installed directly with:  1 sudo apt install ros-noetic-gmapping  In case you want to try more advanced SLAM algorithms, such as karto_slam or cartographer_ros you need the following Ubuntu package dependencies. Alternatively you can install from source by building the cloned git repository in your catkin workspace. Take the required installation size into account. For example karto_slam needs approximately 125MB because it will also install ros-noetic-open-karto.  1 sudo apt install ros-noetic-slam-karto  ### SLAM SLAM stands for Simultaneous Localization and Mapping sometimes refered to as Concurrent Localization and Mappping (CLAM). The SLAM algorithm combines localization and mapping, where a robot has access only to its own movement and sensory data. The robot must build a map while simultaneously localizing itself relative to the map. See also this blog post on FastSLAM. To use the following slam algorithms, we need a mobile robot that provides odometry data and is equipped with a horizontally-mounted, fixed, laser range-finder. Viewed on a higher level, every specific slam node of these algorithms will attempt to transform each incoming scan into the odom (odometry) tf frame. Therefore the node will subscribe to the laser /scan and the /tf topics. Transforms are necessary to relate frames for laser, base, and odometry. The only exception is hector_slam which doesn't require odometry for mapping. The following SLAM implementations are offered using the launch files explained in the next section. It is suggested to start with gmapping which is used by default. • gmapping: This package contains a ROS wrapper for OpenSlam's Gmapping. The gmapping package provides laser-based SLAM (Simultaneous Localization and Mapping), as a ROS node called slam_gmapping. Using slam_gmapping, you can create a 2-D occupancy grid map (like a building floorplan) from laser and pose data collected by a mobile robot. • cartographer: Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations. See the documentation for an algorithm walkthrough. • karto: This package pulls in the Karto mapping library, and provides a ROS wrapper for using it. Karto is considered more accurate than, for example gmapping (note: for ROS noetic, see slam_karto) and became open source in 2010. • hector_slam: metapackage that installs hector_mapping and related packages. The hector_mapping is a SLAM approach that can be used without odometry as well as on platforms that exhibit roll/pitch motion (of the sensor, the platform or both), such as drones. It leverages the high update rate of modern LIDAR systems like the Hokuyo UTM-30LX and provides 2D pose estimates at scan rate of the sensors (40Hz for the UTM-30LX). While the system does not provide explicit loop closing ability, it is sufficiently accurate for many real world scenarios. The system has successfully been used on Unmanned Ground Robots, Unmanned Surface Vehicles, Handheld Mapping Devices and logged data from quadrotor UAVs. Unlike gmapping which uses a particle filter, karto, cartographer and hector_slam are all graph-based SLAM algorithms. The least accurate SLAM algorithm is gmapping but it works fine for smaller maps. Use other algorithms, such as karto if you operate your robot in larger environments or you want more accuracy. Another interesing package is slam_toolbox which provides ROS1 and ROS2 support and is based on the easy to use karto algorithm. karto is the basis for many companies because it provides an excellent scan matcher and can operate in large environments. Additionally, slam_toolbox provides tools to edit a generated map and even create a high quality map using stored data (offline). The cartographer package is currently supported by OpenRobotics and not by Google where it was originally developed. It is currently also not setup correctly for DiffBot. Using it will result in errors. #### Launch files This package provides a main launch file named diffbot_slam.launch which accepts an argument slam_method. Depending on its value, different launch files will be included that execute the specified SLAM algorithm using its configuration in the config folder. As mentioned above, every ROS slam package requries messages from the laser-range finder topic. Usually this topic is named /scan. To distinguish possible multiple lidars, the topic of DiffBot resides in its namespace /diffbot/scan. Therefore, its necessary to remap the /scan topic to /diffbot/scan. The following shows how this was done for the gmapping launch file. Inside this package in the launch/gmapping.launch it is important to map the scan topic to laser scanner topic published by Diffbot. Remappings are done in the node tag. Here, for the gmapping.launch in the gmapping node:   1 2 3 4 5 6 7 8 9 10 11  ... ...  #### Parameter Configurations Most of the configrations are the same as turtlebot3_slam/config. For detailed description of what each parameter does, please check the individual package documentation of the different SLAM methods. ### Gazebo Simulation Tests To test SLAM in the Gazebo simulator run the following two launch files in separate terminals. First run the simulation with:  1 roslaunch diffbot_gazebo diffbot.launch world_name:='$(find diffbot_gazebo)/worlds/turtlebot3_world.world' 

and in a second terminal execute the SLAM algorithm:

 1 roslaunch diffbot_slam diffbot_slam.launch slam_method:=gmapping 

Here you can choose between different algorithms by changing the value of the slam_method argument. Possible values are gmapping (the default), karto, hector and cartographer.

The ROS node graph will look like the following:

In the figure we can see that gmapping subscribes and publishes to tf.

It requires the transformation from <the frame attached to incoming scans> to the base_link, which is usually a fixed value, broadcast periodically by the robot_state_publisher. Aditionally, it requires the transform from base_link to odom. This is provided by the odometry system (e.g., the driver for the mobile base). In the case of DiffBot the odometry system consists of EKF fusion data from the motor encoders and the IMU. The provided tf transforms are map to odom that describes the current estimate of the robot's pose within the map frame. You can read more about the required and provided transforms in the documentation.

### Field Tests

In case you get inaccurate maps follow the official ROS troubleshooting guide for navigation.

### Frontier Exploration

The so far described mapping approaches require manually steering the robot in the unknown environment. Frontier exploration is an approach to move a mobile robot on its own to new frontiers to extend its map into new territory until the entire environment has been explored.

The ROS wiki provides a good tutorial using Husky robot how to use the frontier_exploration package. A lightweight alternative is the explore_lite package.

### Other SLAM Packages (for 3D Mapping)

• hdl_graph_slam: Open source ROS package for real-time 6DOF SLAM using a 3D LIDAR. It is based on 3D Graph SLAM with NDT scan matching-based odometry estimation and loop detection. This method is useful for outdoor.
• RTAB-Map: stands for Real-Time Appearance-Based Mapping and is a RGB-D SLAM approach based on a global loop closure detector with real-time constraints. This package can be used to generate a 3D point clouds of the environment and/or to create a 2D occupancy grid map for navigation. To do this it requires only a stereo or RGB-D camera for visual odometry. Additional wheel odometry is not required but can improve the result.
• Loam Velodyne: Laser Odometry and Mapping (Loam) is a realtime method for state estimation and mapping using a 3D lidar, see also the forked Github repository for loam_velodyne. Note that this is not supported officially anymore because it became closed source.
• ORB-SLAM2: Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities. See orb_slam2_ros for the ROS wrapper.
• slam_toolbox: This package provides a sped up improved slam karto with updated SDK and visualization and modification toolsets. It is a ROS drop in replacement to gmapping, cartographer, karto, hector, etc. This package supports ROS1 and ROS2 and is suitable for use in commercial products because it can map large environments. And it provides tools to edit the generated maps. See also the related ROSCon 2019 video.

Papers: