19 Mapping & Navigation Course

19.1 Mapping

19.1.1 URDF Model

  • URDF Model Introduction

The Unified Robot Description Format (URDF) is an XML file format widely used in ROS (Robot Operating System) to comprehensively describe all components of a robot.

Robots are typically composed of multiple links and joints. A link is defined as a rigid object with certain physical properties, while a joint connects two links and constrains their relative motion.

By connecting links with joints and imposing motion restrictions, a kinematic model is formed. The URDF file specifies the relationships between joints and links, their inertial properties, geometric characteristics, and collision models.

  • Comparison between Xacro and URDF Model

The URDF model serves as a description file for simple robot models, offering a clear and easily understandable structure. However, when it comes to describing complex robot structures, using URDF alone can result in lengthy and unclear descriptions.

To address this limitation, the xacro model extends the capabilities of URDF while maintaining its core features. The xacro format provides a more advanced approach to describe robot structures. It greatly improves code reusability and helps avoid excessive description length.

For instance, when describing the two legs of a humanoid robot, the URDF model would require separate descriptions for each leg. On the other hand, the xacro model allows for describing a single leg and reusing that description for the other leg, resulting in a more concise and efficient representation.

  • Basic Syntax of URDF Model

1. XML Basic Syntax

The URDF model is written using XML standard.

Elements:

An element can be defined as desired using the following formula:

<element>

</element>

Properties:

Properties are included within elements to define characteristics and parameters. Please refer to the following formula to define an element with properties:

<element

property_1=”property value1”

property_2=”property value2”>

</element>

Comments:

Comments have no impact on the definition of other properties and elements. Please use the following formula to define a comment:

<!– comment content –>

2. Link

The Link element describes the visual and physical properties of the robot’s rigid component. The following tags are commonly used to define the motion of a link:

<visual>: Describe the appearance of the link, such as size, color and shape.

<inertial>: Describe the inertia parameters of the link, which will used in dynamics calculation.

<collision>: Describe the collision inertia property of the link

Each tag contains the corresponding child tag. The functions of the tags are listed below.

Tag Function
origin Describe the pose of the link. It contains two parameters, including xyz and rpy. Xyz describes the pose of the link in the simulated map. Rpy describes the pose of the link in the simulated map.
mess Describe the mess of the link
inertia Describe the inertia of the link. As the inertia matrix is symmetrical, these six parameters need to be input, ixx, ixy, ixz, iyy, iyz and izz, as properties. These parameters can be calculated.
geometry Describe the shape of the link. It uses mesh parameter to load texture file, and em[ploys filename parameters to load the path for texture file. It has three child tags, namely box, cylinder and sphere.
material Describe the material of the link. The parameter name is the required filed. The tag color can be used to change the color and transparency of the link.

3. Joint

The “Joint” tag describes the kinematic and dynamic properties of the robot’s joints, including the joint’s range of motion, target positions, and speed limitations. In terms of motion style, joints can be categorized into six types.

The following tags will be used to write joint motion.

<parent_link>: Parent link

<child_link>: Child link

<calibration>: Calibrate the joint angle

<dynamics>: Describes some physical properties of motion

<limit>: Describes some limitations of the motion

The function of each tag is listed below. Each tag involves one or several child tags.

Tag Function
origin Describe the pose of the parent link. It involves two parameters, including xyz and rpy. Both xyz and rpy describe the pose of the link in simulated map.
axis Control the child link to rotate around any axis of the parent link.
limit The motion of the child link is constrained using the lower and upper properties, which define the limits of rotation for the child link. The effort properties restrict the allowable force range applied during rotation (values: positive and negative; units: N). The velocity properties confine the rotational speed, measured in meters per second (m/s).
mimic Describe the relationship between joints.
safety_controller Describes the parameters of the safety controller used for protecting the joint motion of the robot.

4. Robot Tag

The complete top tags of a robot, including the <link> and <joint> tags, must be enclosed within the <robot> tag. The format is as follows:

5. gazebo Tag

This tag is used in conjunction with the Gazebo simulator. Within this tag, you can define simulation parameters and import Gazebo plugins, as well as specify Gazebo’s physical properties, and more.

6. Write Simple URDF Model

Name the model of the robot

To start writing the URDF model, we need to set the name of the robot following this format: “<robot name=“robot model name”>”. Lastly, input “</robot>” at the end to represent that the model is written successfully.

7. Set links

  1. To write the first link and use indentation to indicate that it is part of the currently set model. Set the name of the link using the following format: <link name=”link name”>. Finally, conclude with “</link>” to indicate the successful completion of the link definition.

  2. Write the link description and use indentation to indicate that it is part of the currently set link, and conclude with </visual>.

  3. The “<geometry>” tag is employed to define the shape of a link. Once the description is complete, include “</geometry>”. Within the “<geometry>” tag, indentation is used to specify the detailed description of the link’s shape. The following example demonstrates a link with a cylindrical shape: “<cylinder length=”0.01” radius=”0.2”/>”. In this instance, “length=”0.01”” signifies a length of 0.01 meters for the link, while “radius=”0.2”” denotes a radius of 0.2 meters, resulting in a cylindrical shape.

  4. The “<origin>” tag is utilized to specify the position of a link, with indentation used to indicate the detailed description of the link’s position. The following example demonstrates the position of a link: “<origin rpy=”0 0 0” xyz=”0 0 0” />”. In this example, “rpy” represents the roll, pitch, and yaw angles of the link, while “xyz” represents the coordinates of the link’s position. This particular example indicates that the link is positioned at the origin of the coordinate system.

  5. The “<material>” tag is used to define the visual appearance of a link, with indentation used to specify the detailed description of the link’s color. To start describing the color, include “<material>”, and end with “</material>” when the description is complete. The following example demonstrates setting a link color to yellow: “<color rgba=”1 1 0 1” />”. In this example, “rgba=”1 1 0 1”” represents the color threshold for achieving a yellow color.

8. Set joint

  1. To write the first joint, use indentation to indicate that the joint belongs to the current model being set. Then, specify the name and type of the joint as follows: “<joint name=”joint name” type=”joint type”>”. Finally, include “</joint>” to indicate the completion of the joint definition.

    Note: to learn about the type of the joint, please refer to “4.2 joint”.

  2. Write the description section for the connection between the link and the joint. Use indentation to indicate that it is part of the currently defined joint. The parent parameter and child parameter should be set using the following format: “<parent link=”parent link”/>”, and “<child link=”child link” />”. With the parent link serving as the pivot, the joint rotates the child link.

  3. “<origin>” describes the position of the joint using indention. This example describes the position of the joint: “<origin xyz=“0 0 0.1” />”. xyz is the coordinate of the joint.

  4. “<axis>” describes the position of the joint adopting indention. “<axis xyz=“0 0 1” />” describes one posture of a joint. xyz specifies the pose of the joint.

  5. “<limit>” imposes restrictions on the joint using indention. The below picture The “<limit>” tag is used to restrict the motion of a joint, with indentation indicating the specific description of the joint angle limitations. The following example describes a joint with a maximum force limit of 300 Newtons, an upper limit of 3.14 radians, and a lower limit of -3.14 radians. The settings are defined as follows: “effort=“joint force (N)”, velocity=“joint motion speed”, lower=“lower limit in radians”, upper=“upper limit in radians”.

  6. “<dynamics>” describes the dynamics of the joint using indention. “<dynamics damping=“50” friction=“1” />” describes dynamics parameters of a joint.

    The complete codes are as below.

19.1.2 Explanation of ROS Robot URDF Model

  • Preparation

To grasp the URDF model, check out “19.1.1 URDF Model->Basic Syntax of URDF Model” for the key syntax. This part quickly breaks down the robot model code and its components.

  • Check Code of Robot Model

  1. Start the robot, and access the robot system desktop using NoMachine.

  2. Click-on to open the ROS1 command-line terminal.

  3. Run the command and hit Enter to disable the app auto-start service.

    sudo systemctl stop start_app_node.service

  4. Click-on to open the ROS2 command-line terminal.

  5. Execute the command and hit Enter key to navigate to the folder containing startup programs.

    colcon_cd jetrover_description

  6. Enter the command “cd urdf” to navigate to the robot simulation model folder.

  7. Execute the command to access the robot simulation model folder.

    vim jetrover.xacro

  8. Locate the code below:

    Several URDF models are combined to create a full robot:

File Name Device
materials Color
inertial_matrix Inertia matrix
lidar_a1 A1 Lidar
lidar_g4 G4 Lidar
car_mecanum Mecanum-wheel chassis
car_tank Track chassis
car_acker Ackermann chassis
Imu Inertial measurement unit
depth_camera Depth camera
common Shared or common components or attributes
connect Connector, describing the physical connection relationships between various components of the robot
arm Robot arm
gripper Gripper
arm.transmission Transmission structure of the robotic arm
gripper.transmission Transmission structure of the gripper

  • Brief Analysis of Robot’s Main Body Model

JetRover has three types of chassis: wheel, track, and Ackerman chassis. Here, we’ll focus on the Ackerman chassis as an example. Although the main model files for the wheel and track chassis are mostly the same, any differences will be explained later in this article.

To begin, open a new command terminal and type “vim car_acker.urdf.xacro” to access the robot model file, which contains the description of each part of the robot model.

vim car_acker.urdf.xacro

<?xml version="1.0" encoding="utf-8"?><robot name="hiwonder" xmlns:xacro="http://ros.org/wiki/xacro">

This is the beginning of the URDF file. It specifies the XML version and encoding, and defines a robot model named “hiwonder”. The xmlns:xacro namespace is utilized here to generate URDF using Xacro macro definitions.

The following line of code defines a Xacro property named “M_PI” and assigns the value of π to it.

 <xacro:property name="M_PI" value="3.1415926535897931"/>

In this section, a link named “base_footprint” is defined as the robot’s chassis.

 <link name="base_footprint"/>

Various characteristics of the robot such as mass, width, height, and depth are specified.

  <xacro:property name="base_link_mass"     value="1.6" /> 
  <xacro:property name="base_link_w"        value="0.30"/>
  <xacro:property name="base_link_h"        value="0.15"/>
  <xacro:property name="base_link_d"        value="0.10"/>

Next, a joint called base_joint is defined with a type of “fixed”, indicating it’s a stationary joint. It connects the parent link base_footprint with the child link “base_link”.

The joint’s position (origin) is determined using an xyz attribute, where ${0.05 + 0.0654867643253843} represents a calculated result.

Following this, a link named w_link is defined to represent a link component.

Lastly, a joint named w_joint is defined with type “fixed”, also being a stationary joint. It connects base_link as the parent link and w_link as the child link. Default values are used for the joint’s origin and axis.

<joint name="base_joint" type="fixed">
    <parent link="base_footprint"/>
    <child link="base_link"/>
    <origin xyz="0.0 0.0 ${0.05 + 0.0654867643253843}" rpy="0 0 0"/>
  </joint>
  <link name="w_link"/>
  <joint
    name="w_joint"
    type="fixed">
    <origin
      xyz="0 0 0"
      rpy="0 0 0" />
    <parent
      link="base_link" />
    <child
      link="w_link" />
    <axis
      xyz="0 0 0" />
  </joint>

This XML code defines a link within a robot model. Let’s dissect it to understand its structure and purpose.

The code begins with the <link> tag, naming the link base_green_link. Inside this tag, there are three main sections: <inertial>, <visual>, and <collision>.

The <inertial> section specifies the link’s inertial properties, like mass and inertia. It includes the <origin> tag, indicating the position and orientation of the inertial coordinate system relative to the link’s coordinate system. The <mass> tag denotes the link’s mass, and the <inertia> tag defines its moment of inertia about its principal axis.

The <visual> section determines the visual representation of the link. It includes the <origin> tag for the visualization coordinate system’s position and orientation relative to the link. The <geometry> tag defines the visual shape, here a grid, while the <mesh> tag specifies the file name of the mesh representing the link’s visual appearance. Lastly, the <material> tag specifies the color or texture, such as “green”.

The <collision> section specifies the link’s collision properties. It resembles the <visual> section but is for collision detection, not visualization. It includes the <origin> and <geometry> tags for defining position, direction, and shape of the collision representation.

In summary, this code defines a link in the robot model, detailing its inertial, visual, and collision properties. In simulations or visualizations, the mesh files specified in the <visual> and <collision> sections are utilized for visual representation and collision detection with other links.

 <link
    name="base_link">
    <xacro:box_inertial m="${base_link_mass}" w="${base_link_w}" h="${base_link_h}" d="${base_link_d}" x="0" y="0" z="-0.15" />
    <visual>
      <origin
        xyz="0 0 0"
        rpy="0 0 0" />
      <geometry>
        <mesh
          filename="package://jetrover_description/meshes/acker/base_link.stl" />
      </geometry>
      <material name="black"/>
    </visual>
    <collision>
      <origin
        xyz="0 0 0"
        rpy="0 0 0" />
      <geometry>
        <mesh
          filename="package://jetrover_description/meshes/acker/base_link.stl" />
      </geometry>
    </collision>
  </link>

This code defines a joint called base_green_joint, which is of type “fixed”, indicating it’s stationary. It connects the parent link base_link to the child link base_green_link. The joint’s coordinate origin is at (0, 0, 0), with Euler angles (rpy) set to (0, 0, 0), and no specific axis is defined for the joint.

<joint
    name="base_green_joint"
    type="fixed">
    <origin
      xyz="0 0 0"
      rpy="0 0 0" />
    <parent
      link="base_link" />
    <child
      link="base_green_link" />
    <axis
      xyz="0 0 0" />
  </joint>

Next, let’s examine the link description:

 <link
    name="wheel_left_front_link">
    <inertial>
      <origin
        xyz="-0.213334280297957 -0.203369638948946 -0.000334615151787748"
        rpy="0 0 0" />
      <mass
        value="0.124188560741815" />
      <inertia
        ixx="0.000108426797272382"
        ixy="4.75710998528631E-10"
        ixz="-4.51221408656634E-09"
        iyy="0.000198989755900859"
        iyz="1.62770134465076E-11"
        izz="0.000108425820065752" />
    </inertial>
    <visual>
      <origin
        xyz="0 0 0"
        rpy="0 0 0" />
      <geometry>
        <mesh
          filename="package://jetrover_description/meshes/acker/wheel_left_front_link.stl" />
      </geometry>
      <material name="black"/>
    </visual>
    <collision>
      <origin
        xyz="0 0 0"
        rpy="0 0 0" />
      <geometry>
        <mesh
          filename="package://jetrover_description/meshes/acker/wheel_left_front_link.stl" />
      </geometry>
    </collision>
  </link>

The provided code describes a link named wheel_left_front_link. This link includes inertial, visual, and collision information.

Inertial properties specify the link’s mass and inertial matrix. The mass is 0.124188560741815, and specific values are assigned to each component of the inertia matrix.

Visual properties define the link’s appearance using a three-dimensional model (mesh) with the file name “package://hiwonder_description/meshes/acker/wheel_left_front_link.stl”. Additionally, the link is colored with a material named “black”.

Collision properties determine the link’s collision shape, also utilizing a 3D model with the same file name as the visual representation.

<joint
    name="wheel_left_back_joint"
    type="fixed">
    <origin
      xyz="0.106753653016542 -0.0920004072996187 -0.0658214520621466"
      rpy="0 0 3.14158803228829" />
    <parent
      link="base_link" />
    <child
      link="wheel_left_back_link" />
    <axis
      xyz="0 0 0" />
  </joint>

The following XML code defines a joint and a link within a robot model.

Joint Definition:

  1. Name: “wheel_left_back_joint”

  2. Type: “fixed” (indicating a fixed joint)

  3. Origin: Specifies the joint’s position and orientation relative to the parent link (“base_link”).

  4. Parent Link: Specifies the parent link of the joint.

  5. Sublink: Specifies the sublink of the joint.

  6. Axis: Specifies the axis of rotation of the joint. Here, it’s set to (0, 0, 0), indicating it’s a fixed joint and doesn’t rotate.

Link Definition:

  1. Name: “wheel_left_back_link”

  2. Inertia: Specifies inertial properties of the link (mass, center of mass, and inertia).

  3. Visualization: Specifies a visual representation of the link, including its position, orientation, geometry (grid), and material.

  4. Collision: Specifies the link’s collision properties, including its position, orientation, and geometry (mesh).

 <joint
    name="wheel_left_back_joint"
    type="fixed">
    <origin
      xyz="0.106753653016542 -0.0920004072996187 -0.0658214520621466"
      rpy="0 0 3.14158803228829" />
    <parent
      link="base_link" />
    <child
      link="wheel_left_back_link" />
    <axis
      xyz="0 0 0" />
  </joint>

Link Definition:

  1. Name: “wheel_right_front_link”

  2. Inertia: Specifies the link’s inertial properties, including mass and inertia matrix.

  3. Visualization: Defines the link’s visual appearance, including its position, orientation, geometry (mesh), and material.

  4. Collision: Describes the link’s collision properties, including its position, orientation, and geometry (mesh).

Specifically:

  1. Inertial: Specifies the mass and inertia matrix of the link. In this example, the link has a mass of 0.124186629923608, and specific values are assigned to individual components of the inertia matrix (ixx, ixy, ixz, iyy, iyz, izz).

  2. Visual: Specifies the visual representation of the link. It utilizes a mesh file located at “package://hiwonder_description/meshes/acker/wheel_right_front_link.stl”, and the material used for visualization is called “black”.

  3. Collision: Defines the collision properties of the link, which are identical to its visual properties and utilize the same mesh file.

<link
    name="wheel_right_front_link">
    <inertial>
      <origin
        xyz="-0.213332807255404 0.203675464611681 -0.000332210127461456"
        rpy="0 0 0" />
      <mass
        value="0.124186629923608" />
      <inertia
        ixx="0.000108439206695888"
        ixy="6.85847929033569E-10"
        ixz="-1.92360226002088E-08"
        iyy="0.00019900428610234"
        iyz="-1.53831301269727E-09"
        izz="0.00010842875904821" />
    </inertial>
    <visual>
      <origin
        xyz="0 0 0"
        rpy="0 0 0" />
      <geometry>
        <mesh
          filename="package://jetrover_description/meshes/acker/wheel_right_front_link.stl" />
      </geometry>
      <material name="black"/>
    </visual>
    <collision>
      <origin
        xyz="0 0 0"
        rpy="0 0 0" />
      <geometry>
        <mesh
          filename="package://jetrover_description/meshes/acker/wheel_right_front_link.stl" />
      </geometry>
    </collision>
  </link>

The code below describes a fixed joint connecting two links named base_link and wheel_right_front_link. The initial position and rotation of the joint are specified by the <origin> tag.

Attributes:

  1. name=”wheel_right_front_joint”: Specifies the joint’s name as “wheel_right_front_joint”.

  2. type=”fixed”: Indicates the joint type as “fixed”, meaning it’s stationary and cannot move.

  3. <origin> tag: Defines the initial position and rotation of the joint. It sets the joint’s xyz coordinates as “-0.10658 0.091851 -0.065487” and the rpy rotation angles as “0 0 3.1416”.

  4. <parent> tag: Specifies the parent link of the joint, which is “base_link”.

  5. <child> tag: Specifies the child link of the joint, which is “wheel_right_front_link”.

  6. <axis> tag: Defines the joint axis. In this case, it’s set to (0, 0, 0), indicating no specific axis is defined.

  <joint
    name="wheel_right_front_joint"
    type="fixed">
    <origin
      xyz="-0.10658 0.091851 -0.065487"
      rpy="0 0 3.1416" />
    <parent
      link="base_link" />
    <child
      link="wheel_right_front_link" />
    <axis
      xyz="0 0 0" />
  </joint>

Below is the description of a link named “wheel_right_back_link”, including its inertial properties, visual properties, and collision properties. The link’s geometry utilizes a mesh file, and its visual properties specify a black material.

  1. -name=”wheel_right_back_link”: This attribute specifies the name of the link, which is “wheel_right_back_link”.

  2. <inertial> tag: This tag defines the link’s inertial properties, including mass and inertia matrix.

  3. <origin> tag: This tag defines the position and rotation of the link’s inertial origin. Specifically, it specifies the xyz coordinates of the link as “0.21333 0.20224 0.00032742” and rpy rotation angles as “0 0 0”.

  4. <mass> tag: This tag defines the mass of the link, specifically as “0.13231”.

  5. <inertia> tag: This tag defines the inertia matrix of the link, including the values of ixx, ixy, ixz, iyy, iyz, and izz.

  6. <visual> tag: This tag defines the visual properties of the link, including appearance and material.

  7. <origin> tag: This tag defines the position and rotation of the link’s visual origin. Specifically, it specifies the xyz coordinates of the link as “0 0 0” and rpy rotation angles as “0 0 0”.

  8. <geometry> tag: This tag defines the geometry of the link, including a mesh file.

  9. <mesh> tag: This tag specifies the mesh file used for the link’s geometry, specifically “package://jetrover_description/meshes/acker/wheel_right_back_link.stl”.

  10. <material> tag: This tag defines the material of the link, specifically “black”.

  11. <collision> tag: This tag defines the collision properties of the link, including geometry.

  12. <origin> tag: This tag defines the position and rotation of the link’s collision origin. Specifically, it specifies the xyz coordinates of the link as “0 0 0” and rpy rotation angles as “0 0 0”.

  13. <geometry> tag: This tag defines the geometry of the link, including a mesh file.

  14. <mesh> tag: This tag specifies the mesh file used for the link’s geometry, specifically “package://jetrover_description/meshes/acker/wheel_right_back_link.stl”

 <link
    name="wheel_right_back_link">
    <inertial>
      <origin
        xyz="0.21333 0.20224 0.00032742"
        rpy="0 0 0" />
      <mass
        value="0.13231" />
      <inertia
        ixx="0.00010909"
        ixy="-8.0297E-09"
        ixz="1.1828E-08"
        iyy="0.00019967"
        iyz="-6.196E-09"
        izz="0.00010906" />
    </inertial>
    <visual>
      <origin
        xyz="0 0 0"
        rpy="0 0 0" />
      <geometry>
        <mesh
          filename="package://jetrover_description/meshes/acker/wheel_right_back_link.stl" />
      </geometry>
      <material name="black"/>
    </visual>
    <collision>
      <origin
        xyz="0 0 0"
        rpy="0 0 0" />
      <geometry>
        <mesh
          filename="package://jetrover_description/meshes/acker/wheel_right_back_link.stl" />
      </geometry>
    </collision>
  </link>

The following code describes a joint named “wheel_right_back_joint”.

<joint>: This is the opening tag for a joint element. name=”wheel_right_back_joint”.

type=”fixed”: This joint’s type is “fixed”, meaning it’s a fixed joint and doesn’t allow movement.

<origin>: This is the opening tag for an origin element, used to define the position and pose of the joint’s origin.

xyz=”0.10675 0.091848 -0.065821”: The position of this joint’s origin in 3D space is (0.10675, 0.091848, -0.065821).

rpy=”0 0 3.1416”: The orientation of this joint’s origin is represented using Euler angles, where the roll, pitch, and yaw are 0, 0, and 3.1416, respectively.

<parent>: This is the opening tag for a parent link element, used to define the joint’s parent link.

link=”base_link”: The parent link of this joint is named “base_link”.

<child>: This is the opening tag for a child link element, used to define the joint’s child link.

link=”wheel_right_back_link”: The child link of this joint is named “wheel_right_back_link”.

<axis>: This is the opening tag for an axis element, used to define the joint’s axis.

xyz=”0 0 0”: The axis of this joint is (0, 0, 0), indicating that no axis is defined.

</joint>: This is the closing tag for the joint element.

<joint
    name="wheel_right_back_joint"
    type="fixed">
    <origin
      xyz="0.10675 0.091848 -0.065821"
      rpy="0 0 3.1416" />
    <parent
      link="base_link" />
    <child
      link="wheel_right_back_link" />
    <axis
      xyz="0 0 0" />
  </joint>

19.1.3 Principles of SLAM Map Building

  • Introduction to SLAM

SLAM stands for Simultaneous Localization and Mapping.

Localization involves determining the pose of a robot in a coordinate system,, The origin of orientation of the coordinate system can be obtained from the first keyframe, existing global maps, landmarks or GPS data.

Mapping involves creating a map of the surrounding environment perceived by the robot. The basic geometric elements of the map are points. The main purpose of the map is for localization and navigation. Navigation can be divided into guidance and control. Guidance includes global planning and local planning, while control involves controlling the robot’s motion after the planning is done.

  • SLAM Mapping Principle

  1. Preprocessing: Optimizing the raw data from the radar point cloud, filtering out problematic data or performing filtering. Using laser as a signal source, pulses of laser emitted by the laser are directed at surrounding obstacles, causing scattering.

Some of the light waves will reflect back to the receiver of the lidar, and then, according to the principle of laser ranging, the distance from the lidar to the target point can be obtained.

Regarding point clouds: In simple terms, the surrounding environment information obtained by lidar is called a point cloud. It reflects a portion of what the ‘eyes’ of the robot can see in the environment where it is located. The object information collected presents a series of scattered, accurate angle, and distance information.

  1. Matching: Matching the point cloud data of the current local environment with the established map to find the corresponding position.

  2. Map Fusion: Integrating new round data from the lidar into the original map, ultimately completing the map update.

  • Notes

  1. Begin the mapping process by positioning the robot in front of a straight wall or within an enclosed box. This enhances the Lidar’s capacity to capture a higher density of scanning points.

  2. Initiate a 360-degree scan of the environment using the Lidar to ensure a comprehensive survey of the surroundings. This step is crucial to guarantee the accuracy and completeness of the resulting map.

  3. For larger areas, it’s recommended to complete a full mapping loop before focusing on scanning smaller environmental details. This approach enhances the overall efficiency and precision of the mapping process.

  • Judge Mapping Result

Finally, assess the robot’s navigation process against the following criteria once the mapping is complete:

  1. Ensure that the edges of obstacles within the map are distinctly defined.

  2. Check for any disparities between the map and the actual environment, such as the presence of closed loops or inconsistencies.

  3. Verify the absence of gray areas within the robot’s motion area, indicating areas that haven’t been adequately scanned.

  4. Confirm that the map doesn’t incorporate obstacles that won’t exist during subsequent localization.

  5. Validate the map’s coverage of the entire extent of the robot’s motion area.

19.1.4 slam_toolbox Mapping Algorithm

  • Mapping Definition

Slam Toolbox software package combines information from laser rangefinders in the form of LaserScan messages and performs TF transformation from odom-> base link to create a two-dimensional map of space. This software package allows for fully serialized reloadable data and pose graphs of SLAM maps, used for continuous mapping, localization, merging, or other operations. It allows Slam Toolbox to operate in synchronous (i.e., processing all valid sensor measurements regardless of delay) and asynchronous (i.e., processing valid sensor measurements whenever possible) modes.

ROS replaces functionalities like gmapping, cartographer, karto, hector, providing comprehensive SLAM functionality built upon the powerful scan matcher at the core of Karto, widely used and accelerated for this package. It also introduces a new optimization plugin based on Google Ceres. Additionally, it introduces a new localization method called ‘elastic pose-graph localization,’ which takes measured sliding windows and adds them to the graph for optimization and refinement. This allows for tracking changes in local features of the environment instead of considering them as biases, and removes these redundant nodes when leaving an area without affecting the long-term map.

Slam Toolbox is a suite of tools for 2D Slam, including:

  1. Mapping, saving map pgm files

  2. Map refinement, remapping, or continuing mapping on saved maps

  3. Long-term mapping: loading saved maps to continue mapping while removing irrelevant information from new laser point clouds

  4. Optimizing positioning mode on existing maps. Localization mode can also be run without mapping using the ‘laser odometry’ mode

  5. Synchronous, asynchronous mapping

  6. Dynamic map merging

  7. Plugin-based optimization solver, with a new optimization plugin based on Google Ceres

  8. Interactive RVIZ plugin

  9. RVIZ graphical manipulation tools for manipulating nodes and connections during mapping

  10. Map serialization and lossless data storage.

From the above diagram, it can be seen that the process is relatively straightforward. The traditional soft real-time operation mechanism of slam involves processing each frame of data upon entry and then returning.

Relevant source code and WIKI links for KartoSLAM:

KartoSLAM ROS Wiki: http://wiki.ros.org/slam_karto

slam_karto software package: https://github.com/ros-perception/slam_karto

open_karto open-source algorithm: https://github.com/ros-perception/open_karto

Mapping Operation Steps

ROS2 mapping and navigation utilize virtual machine connectivity to enable mapping and navigation with the robot on the same local network.

  • Install Virtual Machine Software and Import the Virtual Machine

1. Install Virtual Machine

Unzip the provided installation files, then click on the installation file to proceed with the installation.

2. Import Virtual Machine Image

  1. Click on to open the virtual machine.

  2. Enter the virtual machine interface and click on “Open Virtual Machine.”

  3. Select the ubuntu_ros2_humble folder extracted locally (6 Virtual Hosts\ubuntu_ros2_humble), choose the Ubuntu 22.04 ROS2.ovf file, and click “Open.”

  4. Enter your desired virtual machine name, select your preferred path, and click “Import.”

  5. The import process is underway, displaying the import progress.

  6. After import completion, follow the prompts to complete the installation process.

3. Copy Robot Files to the Virtual Machine

(1) Export Files from the Robot

  1. Start the robot, and access the robot system desktop using NoMachine.

  2. Click-on to open the ROS1 command-line terminal.

  3. Execute the command to disable the app auto-start service.

    sudo systemctl stop start_app_node.service

  4. Click-on to start the ROS2 command-line terminal.

  5. Enter the command to navigate to the ros2_ws/src/ directory:

    cd ros2_ws/src/

  6. Enter the command to package the three files ‘navigation’, ‘slam’, and ‘simulations’ into a compressed file:

    zip -r src.zip navigation slam simulations

  7. Enter the command to move the compressed file to the shared directory:

    mv src.zip /home/ubuntu/share/tmp

  8. Run the command and return to ros2_ws directory:

    cd ..

  9. Enter the command to view the .typerc file in this directory:

    ls -a

  1. Enter the command to move the .typerc file to the shared directory:

    cp .typerc /home/ubuntu/share/tmp

  2. Click-on to open the file directory, then navigate to the home/docker/tmp directory:

  3. Use the shortcut ‘Ctrl + H’ to show the hidden .typerc file:

  4. Drag the two files from the directory to the computer desktop.

(2) Import Files into the Virtual Machine

  1. Click-on to navigate to the home directory.

  2. Drag and drop the .typerc and src.zip files from the computer desktop into the virtual machine:

4. Create and Compile the Workspace

  1. Click-on to start the virtual machine command-line terminal.

  2. Enter the command to create the ros2_ws/src directory:

    mkdir -p ros2_ws/src

  3. Extract the file ‘src.zip’ to the directory ‘home/ubuntu’.

    unzip src.zip

  4. Move the simulations, slam, and navigation files to the ros2_ws/src directory:

    mv simulations slam navigation /home/ubuntu/ros2_ws/src/

  5. Move the .typerc file to the ros2_ws directory:

    mv .typerc ros2_ws/

  6. Navigate to the ros2_ws directory:

    cd ros2_ws/

  7. Enter the command to check if .typerc has been moved to the ros2_ws directory:

    ls -a

  1. Input the following command to compile the workspace:

    colcon build

  2. Change the .bashrc file, and input the following command:

    gedit ~/.bashrc

Copy the content below to the file .bashrc

source /home/ubuntu/ros2_ws/.typerc
source /home/ubuntu/ros2_ws/install/setup.bash

  1. After finishing writing, use the shortcut Ctrl + S or click the Save button in the upper right corner to save and exit.

  2. Run the command below to refresh the environment configuration.

    source ~/.bashrc

5. Set the Robot to LAN Mode

  1. Click-on to open the ROS1 command-line terminal.

  2. Enter the command to modify the wifi configuration file, change it to LAN mode, and update your own WIFI name and password.

    gedit ~/hiwonder-toolbox/hiwonder_wifi_conf.py

#!/usr/bin/python3
#coding:utf8

HW_WIFI_MODE = 2                     # WiFi operating mode, 1 for AP mode, 2 for STA mode
# HW_WIFI_AP_SSID = 'HW-Robot1'        # SSID in AP mode. Composed of characters and numbers, must start with HW- to enable app functionality
# HW_WIFI_AP_PASSWORD = 'hiwonder'     # WiFi password in AP mode, composed of characters and numbers
HW_WIFI_STA_SSID = 'hiwonder'        # SSID in STA mode
HW_WIFI_STA_PASSWORD = 'hiwonder'    # WiFi password in STA mode

  1. After finishing writing, use the shortcut Ctrl + S or click the Save button in the upper right corner to save and exit.

  2. Enter the command to restart the network, or reboot the system. It is recommended to reboot the system. After that, check the IP address on the robot’s OLED screen for connection.

    sudo systemctl restart hw_wifi.service

  3. It is important to note that the virtual machine and the robot are connected to the same LAN, and their IP addresses should be within the same subnet:

    Virtual Machine:

    Robot:

  4. After rebooting, click on to open the robot’s ROS2 command line terminal. You will notice that the ROS_DOMAIN_ID is 0, which is the same as on the virtual machine.

    Robot:

    Virtual Machine:

6. slam Mapping Instructions

(1) Robot Operations

  1. Click-on to open the ROS1 command-line terminal.

  2. Execute the following command to disable the app auto-start service.

    sudo systemctl stop start_app_node.service

  3. Click-on to open the ROS2 command-line terminal.

  4. Run the command to initiate mapping:

    ros2 launch slam slam.launch.py

(2) Virtual Machine Instructions

  1. Click-on to open the command line terminal of the virtual machine system.

  2. Enter the command to open the RViz tool and display the mapping results.

    ros2 launch slam rviz_slam.launch.py

(3) Enable Keyboard Control

  1. Click-on to open the ROS2 command-line terminal.

  2. Enter the command to start the keyboard control node, and press Enter key:

    ros2 launch peripherals teleop_key_control.launch.py

If you see the prompt as shown in the following image, it means the keyboard control service has been successfully started.

  1. Control the robot to move in the current space to build a more complete map. The table below lists the keyboard keys available for controlling robot movement and their corresponding functions:

Key Robot Action
W Short press to switch to forward state and continuously move forward
S Short press to switch to backward state and continuously move backward
A Long press to interrupt the forward or backward state and turn left
D Long press to interrupt the forward or backward state and turn right

When controlling the robot’s movement with the keyboard to map, it’s advisable to reduce the robot’s movement speed appropriately. The slower the robot’s speed, the smaller the odometry relative error, leading to better mapping results. As the robot moves, the map displayed in RVIZ will continuously expand until the entire environment scene’s map construction is completed.

(4) Save Map

  1. Click-on to open the ROS2 command-line terminal.

  2. Run the following command and hit Enter key:

    cd ~/ros2_ws/src/slam/maps && ros2 run nav2_map_server map_saver_cli -f "map_01" --ros-args -p map_subscribe_transient_local:=true

(5) Effect Optimization

If you desire a more precise mapping outcome, optimizing the odometry can be beneficial. Mapping with the robot requires the use of odometry, which in turn relies on the IMU.

The robot itself comes with pre-calibrated IMU data loaded, enabling it to perform mapping and navigation functions effectively. However, calibrating the IMU can still enhance accuracy further. The calibration method and steps for the IMU can be found in the “3 ROS1-Chassis Motion Control Lesson -> 3.2 Motion Control -> 3.2.1 IMU, Linear Velocity and Angular Velocity Calibration” section.

7. Parameter Explanation

The parameter file can be found in the “ros2_ws\src\slam\config\slam.yaml” directory.

For more detailed information about the parameters, please refer to the official documentation: https://wiki.ros.org/slam_toolbox

8. Launch File Analysis

The launch file is located at:

/home/ubuntu/ros2_ws/src/slam/launch/slam.launch.py

(1) Import Library

The launch library can be explored in detail in the official ROS documentation:

https://docs.ros.org/en/humble/How-To-Guides/Launching-composable-nodes.html

from ament_index_python.packages import get_package_share_directory

from launch_ros.actions import PushRosNamespace
from launch import LaunchDescription, LaunchService
from launch.substitutions import LaunchConfiguration
from launch.launch_description_sources import PythonLaunchDescriptionSource
from launch.actions import DeclareLaunchArgument, IncludeLaunchDescription, GroupAction, OpaqueFunction, TimerAction

(2) Set the Storage Path

Use the get_package_share_directory function to obtain the path of the slam package.

    if compiled == 'True':
        slam_package_path = get_package_share_directory('slam')
    else:
        slam_package_path = '/home/ubuntu/ros2_ws/src/slam'

(3) Initiate Other Launch File

    base_launch = IncludeLaunchDescription(
        PythonLaunchDescriptionSource(
            os.path.join(slam_package_path, 'launch/include/robot.launch.py')),
        launch_arguments={
            'sim': sim,
            'master_name': master_name,
            'robot_name': robot_name
        }.items(),
    )

    slam_launch = IncludeLaunchDescription(
        PythonLaunchDescriptionSource(
            os.path.join(slam_package_path, 'launch/include/slam_base.launch.py')),
        launch_arguments={
            'use_sim_time': use_sim_time,
            'map_frame': map_frame,
            'odom_frame': odom_frame,
            'base_frame': base_frame,
            'scan_topic': '{}/scan'.format(frame_prefix),
            'enable_save': enable_save
        }.items(),
    )

    if slam_method == 'slam_toolbox':
        bringup_launch = GroupAction(
         actions=[
             PushRosNamespace(robot_name),
             base_launch,
             TimerAction(
                 period=5.0,  # 延时等待其它节点启动好(delaying to wait for other nodes to start up properly)
                 actions=[slam_launch],
             ),
          ]
        )

base_launch: Launch for hardware initialization

slam_launch: Launch for basic mapping

bringup_launch: Launch for initial pose setup

19.1.5 RTAB-VSLAM 3D Vision Mapping & Navigation

  • RTAB-VSLAM Description

RTAB-VSLAM is a appearance-based real-time 3D mapping system, it’s an open-source library that achieves loop closure detection through memory management methods. It limits the size of the map to ensure that loop closure detection is always processed within a fixed time limit, thus meeting the requirements for long-term and large-scale environment online mapping.

  • RTAB-VSLAM Working Principle

RTAB-VSLAM 3D mapping employs feature mapping, offering the advantage of rich feature points in general scenes, good scene adaptability, and the ability to use feature points for localization. However, it has drawbacks, such as a time-consuming feature point calculation method, limited information usage leading to loss of image details, diminished effectiveness in weak-texture areas, and susceptibility to feature point matching errors, impacting results significantly.

After extracting features from images, the algorithm proceeds to match features at different timestamps, leading to loop detection. Upon completion of matching, data is categorized into long-term memory and short-term memory. Long-term memory data is utilized for matching future data, while short-term memory data is employed for matching current time-continuous data.

During the operation of the RTAB-VSLAM algorithm, it initially uses short-term memory data to update positioning points and build maps. As data from a specific future timestamp matches long-term memory data, the corresponding long-term memory data is integrated into short-term memory data for updating positioning and map construction.

RTAB-VSLAM software package link: https://github.com/introlab/rtabmap

  • RTAB-VSLAM 3D Mapping Instructions

1. Robot Operations

  1. Click on on the system desktop to open the ROS1 command-line terminal.

  2. Run the command to disable the app auto-start service:

    sudo systemctl stop start_app_node.service

  3. Click-on to open the ROS2 command-line terminal.

  4. Execute the command to start mapping:

    ros2 launch slam rtabmap_slam.launch.py

2. Virtual Machine Operation

  1. Click-on to open the command-line terminal.

  2. Enter the command to open the RViz tool and display the mapping effect:

    ros2 launch slam rviz_rtabmap.launch.py

3. Enable Keyboard Control

  1. Click-on to open the ROS2 command-line terminal.

  2. Enter the command to start the keyboard control node, and press Enter.

    ros2 launch peripherals teleop_key_control.launch.py

If you encounter the prompt as shown in the figure below, it means that the keyboard control service has been successfully activated.

  1. Control the robot to move in the current space to build a more complete map. The table below shows the keyboard keys available for controlling robot movement and their corresponding functions:

Key Robot Action
W Short press to switch to the forward state and continuously move forward
S Short press to switch to the backward state and continuously move backward
A Long press to interrupt the forward or backward state and turn left
D Long press to interrupt the forward or backward state and turn right

When controlling the robot’s movement for mapping using the keyboard, it’s advisable to appropriately reduce the robot’s movement speed. The smaller the robot’s running speed, the smaller the relative error of the odometry, resulting in a better mapping effect. As the robot moves, the map displayed in RVIZ will continuously expand until the entire environmental scene’s map construction is completed.

4. Map Saving

After mapping is completed, you can use the shortcut “Ctrl+C” in each command-line terminal window to close the currently running program.

Note

Note: For 3D mapping, there’s no need to manually save the map. When you use “Ctrl+C” to close the mapping command, the map will be automatically saved.

  • lanunch File Analysis

The launch file is located at:

/home/ubuntu/ros2_ws/src/slam/launch/rtabmap_slam.launch.py

1. Import Library:

You can refer to the ROS official documentation for detailed analysis of the launch library:

“https://docs.ros.org/en/humble/How-To-Guides/Launching-composable-nodes.html”

import os
from ament_index_python.packages import get_package_share_directory

from launch_ros.actions import PushRosNamespace
from launch import LaunchDescription, LaunchService
from launch.substitutions import LaunchConfiguration
from launch.launch_description_sources import PythonLaunchDescriptionSource
from launch.actions import DeclareLaunchArgument, IncludeLaunchDescription, GroupAction, OpaqueFunction, TimerAction

2. Set the Storage Path

Use get_package_share_directory to obtain the path of the slam package.

    if compiled == 'True':
        slam_package_path = get_package_share_directory('slam')
    else:
        slam_package_path = '/home/ubuntu/ros2_ws/src/slam'

3. Initiate Other Launch File

    base_launch = IncludeLaunchDescription(
        PythonLaunchDescriptionSource(
            os.path.join(slam_package_path, 'launch/include/robot.launch.py')),
        launch_arguments={
            'sim': sim,
            'master_name': master_name,
            'robot_name': robot_name,
            'action_name': 'horizontal',
        }.items(),
    )

    rtabmap_launch = IncludeLaunchDescription(
        PythonLaunchDescriptionSource(
            os.path.join(slam_package_path, 'launch/include/rtabmap.launch.py')),
        launch_arguments={
            'use_sim_time': use_sim_time,
        }.items(),
    )

    bringup_launch = GroupAction(
     actions=[
         PushRosNamespace(robot_name),
         base_launch,
         TimerAction(
             period=10.0,  # 延时等待其它节点启动好(delaying to wait for other nodes to start up properly)
             actions=[rtabmap_launch],
         ),
      ]
    )

base_launch: Launch for hardware initialization

slam_launch: Basic mapping launch

rtabmap_launch: RTAB mapping launch

bringup_launch: Initial pose launch