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ROSMASTER R2📖
1.Preparation and First Trial
  • 1.1 Accessories introduction
  • 1.2 Precautions for battery
  • 1.3 First Trial
2.Assembly steps
  • Installaction Video
3.ROS1-Remote control course
  • 3.1 APP remote control
  • 3.2 Model aircraft remote control
  • 3.3 Handle remote control
4.ROS1-Hardware course
  • 4.1 About expansion board_V3.0
  • 4.1 About expansion board_V1.0
  • 4.2 STM32 Development environment
  • 4.3 Button to control the buzzer to whistle
  • 4.4 FreeRTOS application
  • 4.5 Serial communication
  • 4.6 SBUS model aircraft remote control
  • 4.7 CAN bus communication
  • 4.8 RGB colorful light bar
  • 4.9 Timer interrupt to control PWM servo
  • 4.10 Control serial port servos
  • 4.11 Nine-axis attitude sensor to obtain data
  • 4.12 Control motor
  • 4.13 Timer captures the encoder data
  • 4.14.1 Kinematic Analysis of Mecanum Wheel
  • 4.14.2 Kinematics Analysis of Ackermann Car
  • 4.15 PID control robot movement
5.ROS1-Basic course
  • 5.1 Update the firmware
  • 5.2 Turn off the self-starting process at startup
  • 5.3 Install the Rosmaster driver library
  • 5.4 Buzzer whistle
  • 5.5 Control PWM servo
  • 5.6 RGB colorful light bar special effects display
  • 5.7 Control motor
  • 5.8 Control robot movement
  • 5.9 Control Serial Servo
6.ROS1-Linux operating system
  • 6.1 Virtual machine installation and use
  • 6.2 Linux basics
  • 6.3 Remote control
  • 6.4 Multi-machine communication configuration
  • 6.5 Static IP and hotspot mode
  • 6.6 Bind the device ID
  • 6.7 Web page real-time monitoring
  • 6.8 Expansion tutorial
  • 6.9.1 Jetson Nano write sytem
  • 6.9.2 Jetson Xavier NX write system
  • 6.9.3 Jetson TX2 NX write system
  • 6.9.4 Raspberry Pi write system
7.ROS1-Docker-pi5
  • 1、Docker overview and docker installation
  • 2、Common commands for docker image containers
  • 3、Docker images deeply understand and publish images
  • 4、Docker hardware interaction and data processing
  • 5、Enter the bot's docker container
  • 6、Robot development environment construction in Docker
  • 7、How customers can update docker images in the future
8.ROS1-ROS basic course
  • 1.ROS introduction
  • 2.ROS installation
  • 3.ROS common command tools
  • 4.ROS workspace
  • 5.ROS function package
  • 6.ROS node
  • 7.ROS topic publisher
  • 8.ROS topic subscribers
  • 9.ROS service client
  • 10.ROS service server
  • 11.ROS action client
  • 12.ROS action server
  • 13.ROS custom message reception
  • 14.ROS-launch file
  • 15.ROS-TF transformation
  • 16.ROS parameter service
  • 17.ROS-rviz use
  • 18.ROS-rqt tool usage
  • 19.Topic message recording and playback
  • 20.urdf model introduction
  • 21.gazebo introduction
  • 22.ROS distributed communication
9.ROS1-OpenCV courses
  • 9.1 Getting started with Open Source CV
  • 9.2 Geometric transformation
  • 9.3 Image processing and drawing text line segments
  • 9.4 Image beautification
  • 9.5 Opencv application
  • 9.6 AR vision
  • 9.7 AR QR code
  • 9.8 ROS+Opencv foundation
  • 9.9 ROS+Opencv application
  • 9.10 Data transformation and point cloud
  • 9.11 MediaPipe development
10.ROS1-Robot control course
  • 10.1 PID algorithm theory
  • 10.2 Robot PID debugging
  • 10.3 Robot information release
  • 10.4 Robot keyboard control
  • 10.5 Robot handle control
  • 10.6 Robot state estimation
  • 10.7 Robot calibration
  • 10.8 Robot URDF model
11.ROS1-Depth camera course
  • 11.1 How to use the Astra camera
  • 11.2 Astra camera calibration
  • 11.3 Astra Color Tracking
  • 11.4 Astra Object Tracking
  • 11.5 Visual tracking autopilot
  • 11.6 Pure visual 2D mapping and navigation
  • 11.7 RTAB-Map 3D mapping navigation
  • 11.8 ORB_SLAM2 basics
  • 11.9 ORB_SLAM2_Octomap
12.ROS1-Lidar course
  • 11.1 Lidar basic-SLAM
  • 11.1 Lidar basic-EAI
  • 11.2 Lidar avoiding
  • 11.3 Lidar guard
  • 11.4 Lidar follow
  • 11.5 Patrol game
  • 11.6 gmapping mapping
  • 11.7 hector mapping
  • 11.8 karto mapping
  • 11.9 cartographer mapping
  • 11.10 rrt_exploration mappin
  • 11.11 AMCL
  • 11.12 Navigation and avoiding
  • 11.13 APP mapping and navigation
  • 11.14 TEB path planning algorithm
13.ROS1-Multi-robot control course
  • 13.1 Multi-machine handle control
  • 13.2 Multi-machine navigation
  • 13.3 Multi-machine formation
  • 13.4 Multi-machine surround
14.ROS1-Deep learning courses
  • 14.1 KNN recognizes handwritten digits
  • 14.2 Basic use of TensorFlow
  • 14.3 Basic use of PyTorch(jetson)
  • 14.4 yolov5 model training(jetson)
  • 14.5 yolov5+tensorrt acceleration(jetson)
  • 14.6 yolov4-tiny
15.ROS1-Voice control courses
  • 15.0 How to use old voice module in new image
  • 15.1 Introduction of voice control module-NEW
  • 15.2 Voice control module port binding-NEW
  • 15.1 Introduction of voice control module-Old
  • 15.2 Voice control module port binding-Old
  • 15.3 Voice control car movement
  • 15.4 Voice control Autopilot
  • 14.5 Voice control color recognition
  • 15.6 Voice control color tracking
  • 15.7 Voice control multi-point navigation
16.ROS1-Model training Autopilot
  • 16.0 ReadMe
  • 16.1 yolov5 to train traffic signs
  • 16.2 TensorRt acceleration identification mark
  • 16.3 Road sign indication function description
  • 16.4 Collect data
  • 16.5 Training model
  • 16.6 Model transformation and application
  • 16.7 AI Autopilot
17. ROS2-Remote control course
  • 1. APP remote control
  • 2. Model aircraft remote control
  • 3. Handle remote control
18.ROS2-Hardware course
  • 1. Expansion board introduction_V3.0
  • 1. Expansion board introduction_V1.0
  • 2. STM32 development environment build
  • 3. Key control buzzer sounding
  • 4. FreeRTOS applications
  • 5. Serial communication
  • 6. SBUS model airplane remote control
  • 7. CAN bus communication
  • 8. RGB colorful light bar
  • 9. Timer interrupt control PWM servos
  • 10. Control serial servos
  • 11. Nine-axis attitude sensor acquires data
  • 12. Control motor forward and reverse
  • 13. Timer Capture Encoder Data
  • 14. Theory of Robot Kinematics Analysis
  • 14.1.Mecanum wheel
  • 14.2.Ackerman
  • 14.3.4WD
  • 15. PID control of robot motion
19. ROS2-Basic course
  • 1. Update the firmware of the expansion board
  • 2.Close the large self-starting program at boot
  • 3. Install Rosmaster driver library
  • 4. Buzzer sounding
  • 5. Control PWM servo
  • 6. RGB colorful light bar special effects show
  • 7. Control motor forward and reverse
  • 8. Controlling robot motion
  • 9. Control serial servos
20. ROS2-Linux operating systemnew
  • 01. Virtual Machines
  • 02. Linux Basics
  • 03. Remote control
  • 04. Multi-computer communication configuration
  • 05. Static ip and hotspot mode
  • 06. Bind device ID
  • 07.Webpage real-time monitoring
  • 08. Expansion Tutorial
  • 09-1.Jetson Nano write system file
  • 09-2. Raspberry Pi write system file
  • 09-3.Orin write system
  • 10.Jetpack old system upgraded to SUPER systemnew
21.ROS2 -Docker(Nano Pi)
  • 1. docker overview and docker installation
  • 2. docker image container common command
  • 3. docker image in-depth understanding and publishing images
  • 4. docker hardware interaction and data processing
  • 5. Enter the docker container of the robot
  • 6. docker in the robot development environment build
  • 7. Customer's method of updating docker images in the future
22.ROS2- ROS2 Basic Tutorial
  • 1.Introduction to ROS2
  • 2.ROS2 install Humble
  • 3.ROS2 development environment
  • 4.ROS2 workspace
  • 5.ROS2 function package
  • 6.ROS2 node
  • 7.ROS2 topic communication
  • 8.ROS2 service communication
  • 9.ROS2 action communication
  • 10.ROS2 custom interface message
  • 11.ROS2 parameter service case
  • 12.ROS2 meta-function package
  • 13.ROS2 distributed communication
  • 14.ROS2 DDS
  • 15.ROS2 time related API
  • 16.ROS2 common command tools
  • 17.ROS2 rviz2 use
  • 18.ROS2 Rqt Toolbox
  • 19.ROS2 Launch startup file configuration
  • 20.ROS2 recording and playback tool
  • 21.ROS2 URDF model
  • 22.ROS2 Gazebo simulation platform
  • 23.ROS2 TF2 coordinate transformation
23. ROS2-Robot control course
  • 1. PID algorithm theory
  • 2. Robot information release
  • 3. Robot control
  • 4. Robot state estimation
  • 5. Robot calibration
  • 6. URDF model
24. ROS2-Lidar course
  • 1-1. Introduction and use of SLAM lidar
  • 1-2. Introduction and use of 4ROS lidar
  • 2. Lidar obstacle avoidance fun gameplay
  • 3. Lidar tracking fun gameplay
  • 4. Patrol function play
  • 5. gmapping map building algorithm
  • 6. cartographer building algorithm
  • 7. Navigation and obstacle avoidance
  • 8. Rosmaster_R2 using the app to build maps
  • 9. Rosmaster_R2 navigating with the app
25. ROS2-OpenCV courses
  • 1. opencv basic course
  • 2. ROS+opencv application
  • 3. QR code creation and recognition
  • 4. AR Vision
  • 5. mediapipe fun gameplay
  • 6. Patrol line autopilot
26. ROS2-Depth camera courses
  • 1. Depth camera use
  • 2. Camera calibration
  • 3. Color tracking
  • 4. KCF object tracking
  • 5. ORB_SLAM2 Basics
  • 6. ORB_SLAM2 PCL mapping
  • 7. ORB_SLAM2 Octomap mapping
  • 8. RTAB-Map 3D mapping navigation
27.ROS2- Voice control courses
  • 0. How to use old voice module in new image
  • 1.1 Introduction to the module-New
  • 1.2 Voice Control Module Port Binding-New
  • 1.3 Introduction to the module-Old
  • 2. Voice control car movement
  • 3. Voice control autopilot
  • 4. Voice control color recognition
  • 5. Voice control color tracking
  • 6. Voice control multi-point navigation
28.Deep learning courses(SUPER)
  • 1.KNN recognizes handwritten digitsnew
  • 2.Basic use of TensorFlownew
  • 3.Basic use of PyTorch(jetson)new
  • 4.YOLOv11Model conversion(SUPER)new
  • 5.YOLOv11Dataset annotation(SUPER)new
  • 6.YOLOv11Model conversion + tensorrt acceleration(SUPER)new
  • 7.YOLOv11Object recognition(SUPER)new
29. ROS2-Multi-robot control
  • 1. Multi-robot control
  • 2. Multi-robot navigation
LLM tutorial
1.Development environment
  • 0. Model usage instructions
  • 1.Environments deployment
  • 2.Dialogue platform installation
2.Jetson Nano B01
  • 1.Qwen2 model
  • 2.TinyLlama model
  • 3.Phi-3 model
  • 4.Gemma model
  • 5.Orca Mini model
  • 6.StarCoder2 model
  • 7.DeepSeek-R1 model
  • 8.Expand LLaVA-Phi3
3.Jetson Orin NXNANO
  • 1.Llama3 model
  • 2.Qwen2 model
  • 3.Phi-3 model
  • 4.Gemma model
  • 5.WizardLM2 model
  • 6.Codellama model
  • 7.DeepSeek-R1 model
  • 8.Orca Mini model
  • 9.StarCoder2 model
  • 10.TinyLlama model
  • 11.Expand LLaVA
  • 12.Expand LLaVA-Phi3
4.Raspberry Pi 5B
  • 1.Meta AI Llama3 model
  • 2.Qwen2 model
  • 3.Phi-3 model
  • 4.Gemma model
  • 5.WizardLM2 model
  • 6.Meta AI Codellama model
  • 7.DeepSeek-R1 model
  • 8.Expand LLaVA
FAQ
  • 1.Using your own Jetson board, car cannot start normally
Download
  • RemoteCtrl_APP(iOS/Android)
  • Mapping_APP(iOS_Android)
  • Instruction Manual
  • SystemFile_ROS1
  • SystemFile_ROS2
  • SystemFile_ROS2_OrinNANOSUPER
  • SystemFile_ROS2_OrinNXSUPER
  • VM software
  • Code
  • Jetson_Boot_USB.tar.gz
  • Autopilot Annex
  • Other_Annex_File
  • LLM_SystemFile
  • ROS2_Basic_VM
Download ZIP

Welcome to ROSMASTER R2 repository

8. RTAB-Map 3D mapping navigation

8、RTAB-Map 3D mapping navigation

 

8、RTAB-Map 3D mapping navigation8.1、Introduction8.2、Configure before use8.3、Mapping8.4、Navigation8.4.1、Start camera8.4.2、Start rviz to display the map [virtual machine start]8.4.3、Display rtabmap_viz [Virtual machine start]8.4.4、Start navigation node8.4.5、Single point navigation8.2.6、Multipoint navigation8.5、Node resolution8.5.1、Show Computational Graph8.5.2、Rtabmap navigation related node details8.5.3、TF transform

Official website of rtabma: http://introlab.github.io/rtabmap/

rtabmap ros-foxy:https://github.com/introlab/rtabmap/tree/foxy-devel

rtabmap ros-humble:https://github.com/introlab/rtabmap/tree/0.21.10-humble

The operating environment, software and hardware reference configurations are as follows:

  • Reference model:ROSMASTER X3

  • Robot hardware configuration:Arm series master controller, Silan A1 lidar, AstraPro Plus depth camera

  • Robot system:Ubuntu(version no requirement)+ docker(20.10.21 and above)

  • PC virtual machine:Ubuntu(22.04)+ ROS2(Humble)

  • Use scene: use on a relatively clean 2D plane

 

 

8.1、Introduction

rtabmap is a SLAM method based on RGB-D images, which uses a bag-of-words-based global Bayesian loop closure detector to build maps in real-time in large-scale environments.

The characteristics of rtabmap are as follows:

  • It can use a handheld RGBD camera for 6 degrees of freedom RGB-D mapping, or use a robot equipped with lidar for 3 degrees of freedom (2D laser) or 6 degrees of freedom (3D laser) mapping.

  • It uses a memory management mechanism to divide the map into working memory (WM), short-term memory (STM) and long-term memory (LTM) to limit the number of anchor points for closed-loop detection and graph optimization to ensure real-time.

  • It uses a discrete Bayesian filter to estimate the probability of forming a loop and selects which anchors to transfer or retrieve based on weight and time.

  • It uses an incremental dictionary creation method instead of a pre-trained dictionary, which can be adapted to different environments.

  • It can run under ROS or as a standalone library and application.

 

8.2、Configure before use

Note: Since the ROSMASTER series robots are divided into several types of robots and various types of equipment, the factory system has been configured with routines for various types of equipment, but since the product cannot be automatically identified, it is necessary to manually set the machine type and radar model.

For the Raspberry Pi PI5 main control, you need to enter the docker container first, but the Orin main board does not need to enter.

make the following modifications according to the car model, radar type and camera type:

root@ubuntu:/# cd
root@ubuntu:~# vim .bashrc

After the modification is complete, save and exit vim, then execute:

xxxxxxxxxx
root@ubuntu:~# source .bashrc
--------------------------------------------------------
ROS_DOMAIN_ID: 12
my_robot_type: x3 | my_lidar: a1 | my_camera: astraplus
--------------------------------------------------------
root@ubuntu:~#

You can see the current modified car model, radar type and camera type

Note: The depth camera of the car should be directly connected to the main controller, not to the USB-HUB, so as to improve the transmission efficiency of the depth camera and improve the effect of map building and navigation. Otherwise, it may result in the inability to build maps and navigate

 

8.3、Mapping

Note: When building a map, the slower the speed, the better the effect (note that if the rotation speed is slower), the effect will be poor if the speed is too fast.

First of all, port binding operation needs to be done on the host machine [that is, the jetson of the car] [see the port binding tutorial chapter], here mainly use radar, serial port and camera three devices.

Then check whether the radar and the serial device are in the port binding state: on the host machine [that is, on the jetson of the car], refer to the following command to execute the check, and the successful binding is as follows:

Check if the camera is in port binding state.

image-20230621171529181

If it is displayed that the radar, serial port, and camera devices are not bound, you can plug and unplug the USB cable to check again.

 

Raspberry Pi PI5 master needs to enter the docker container first,

Enter the docker container (please refer to [docker course chapter ----- 5. Enter the robot's docker container] for steps),

and execute the following launch file in the terminal:

1、 Start the Astra camera

xxxxxxxxxx
ros2 launch astra_camera astro_pro_plus.launch.xml

2、Start mapping

xxxxxxxxxx
ros2 launch yahboomcar_nav map_rtabmap_launch.py

3、Start rviz to display the map. This step is recommended to be performed in a virtual machine. Multi-machine communication needs to be configured in the virtual machine

xxxxxxxxxx
ros2 launch yahboomcar_nav display_rtabmap_map_launch.py

image-20230621162945020

 

4、 Start the keyboard control node. This step is recommended to be performed in a virtual machine. Multi-machine communication needs to be configured in the virtual machine.Or use the remote control [slowly move the car] to start building the map until the complete map is built.

xxxxxxxxxx
ros2 run yahboomcar_ctrl yahboom_keyboard

image-20230621163801080

 

 

5、Map save

When the map is built, directly [ctrl+c] to exit the map node, and the system will automatically save the map. Map default save path【~/.ros/rtabmap.db】

 

8.4、Navigation

Raspberry Pi PI5 master needs to enter the docker container first,

Enter the docker container (please refer to [docker course chapter ----- 5. Enter the robot's docker container] for steps),

and execute in a terminal,

 

8.4.1、Start camera

xxxxxxxxxx
ros2 launch astra_camera astro_pro_plus.launch.xml

 

8.4.2、Start rviz to display the map [virtual machine start]

Configure multi-machine communication in the Ubuntu virtual machine and the docker container. This step is recommended to start in the virtual machine: to maintain time synchronization and reduce resource consumption, because if you use vnc, it is very dependent on the network, which may cause navigation failure.

【Note that you must first start the node that displays the map, and then start the navigation node in step 3. This is because the navigation2 terminal map topic is only published once. How to start the navigation node first, and then start the rviz display, you may not be able to subscribe to that The only map topic that was released once, causing the map not to be displayed】

xxxxxxxxxx
ros2 launch yahboomcar_nav display_rtabmap_nav_launch.py

image-20230621174839441

 

8.4.3、Display rtabmap_viz [Virtual machine start]

rtabmap_viz is the visual interface of RTAB-Map, which is the package of RTAB-Map GUI graphics library, similar to rviz but has options for RTAB-Map. It can subscribe to different topics, such as odom, rgb/image, depth/image, scan, etc., to display the process and results of SLAM, and load the 3D map into rviz at the same time. This step is recommended to start in a virtual machine.

Note that the map will not be displayed after this step is started, and it will not be displayed until the next step of the navigation node is started.

xxxxxxxxxx
ros2 launch yahboomcar_nav rtabmap_viz_launch.py

image-20230621175513233

 

8.4.4、Start navigation node

Navigation can be divided into single-point navigation and multi-point navigation, which will be introduced below

 

1、Start the navigation node, enter the docker container, and execute in a terminal:

x
ros2 launch yahboomcar_nav navigation_rtabmap_launch.py
#If this step of navigation node fails, start the following command again
ros2 launch yahboomcar_nav navigation_teb_launch.py #teb navigation

2、Click [2D Pose Estimate] on rviz, then compare the pose of the car and mark an initial pose for the car on the map.

image-20230621175842191

标完后的显示如下:image-20230621175731389

 

3、Comparing the overlap between the radar scanning point and the obstacle, you can set the initial pose of the car several times until the radar scanning point and the obstacle roughly coincide;

 

8.4.5、Single point navigation

After the initial pose is set, you can click [2D Goal Pose] to set a navigation target point, and the car will start single-point navigation.

image-20230621180010957

image-20230621180238347

 

8.2.6、Multipoint navigation

1、After the initial pose is set, you can click [Panels] in the upper left corner of rviz --- select [Navigation 2], and the [Navigation 2] panel will be displayed.

 

2、Click [Waypoint mode] in the above picture, then click [Navigation2 Goal] on rviz, you can mark a target point on the map, click [Navigation2 Goal] again, you can mark the second target point on the map. Multiple target points can be marked at one time.

image-20230621180955746

 

3、After marking multiple target points, click [Start Navigation] to start multi-point navigation. After the multi-point navigation is completed, the car will stay at the pose of the last target point.

 

4、It may appear during the navigation process. This is due to the navigation2 itself in the ros-foxy version. The subsequent ros2 version has been fixed.

image-20230421152009718

 

5、During the navigation process, rviz may exit. This may be caused by insufficient resources. You can close the display of rtabmap_viz on the virtual machine after the navigation node starts to fully display the 3D map.

 

 

8.5、Node resolution

 

8.5.1、Show Computational Graph

xxxxxxxxxx
rqt_graph

 

8.5.2、Rtabmap navigation related node details

/rgbd_sync

image-20230621182357851

/rtabmap

image-20230621182433949

image-20230621182529855

image-20230621182550423

/rtabmap_viz

image-20230621182627882

/bt_navigator

image-20230421143315502

/controller_server

image-20230421143540368

/global_costmap/global_costmap

image-20230421143606354

/local_costmap/local_costmap

image-20230421143651486

/planner_server

image-20230421143734845

/recoveries_server

image-20230421143753659

/waypoint_follower

image-20230421143822640

 

 

8.5.3、TF transform

xxxxxxxxxx
ros2 run tf2_tools view_frames.py

image-20230621182209009

 

ORB_SLAM2 Octomap mapping
How to use old voice module in new image

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