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ROSMASTER X3📖
1.About ROSMASTER X3
  • 1.1 Wiring Introduction
  • 1.2 Precautions for battery
  • 1.3 First Trial
2.Assembly steps
  • Installation Video(1-10)
  • Installation Video(11-18)
  • Installation Video(20-28)
  • Installation Video(29-30)
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
  • 12.1 Lidar basic-SLAM
  • 12.1 Lidar basic-4ROS
  • 12.2 Lidar avoiding
  • 12.3 Lidar guard
  • 12.4 Lidar follow
  • 12.5 Patrol game
  • 12.6 gmapping mapping algorithm
  • 12.7 hector mapping algorithm
  • 12.8 karto mapping algorithm
  • 12.9 cartographer mapping algorithm
  • 12.10 rrt_exploration mapping algorithm
  • 12.11 AMCL
  • 12.12 Navigation and avoiding
  • 12.13 APP mapping and navigation
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 control
  • 2. Model aircraft control
  • 3. Wireless Handle Control Tutorial
18.ROS2- Hardware course
  • 1. About expansion board_V1.0
  • 1. About expansion board_V3.0
  • 2. STM32 Development environment
  • 3. Button control
  • 4. FreeRTOS application
  • 5. Serial communication
  • 6. SBUS model aircraft remote control
  • 7. CAN bus communication
  • 8. RGB colorful light bar
  • 9. Timer interrupt to control PWM servo
  • 10. Control serial port servos
  • 11. Nine-axis attitude sensor to obtain data
  • 12. Control motor
  • 13. Timer captures the encoder data
  • 14. Robot kinematics analysis theory
  • 15. PID control robot movement
19.ROS2- ROSMASTER basic course
  • 1. Update the firmware
  • 2. Turn off the self-starting process at startup
  • 3. Install the Rosmaster driver library
  • 4. Buzzer whistle
  • 5. Control PWM servo
  • 6. RGB colorful light bar special effects display
  • 7. Control motor
  • 8. Control robot movement
  • 9. Control Serial Servo
20.ROS2- Linux operating systemnew
  • 1. Virtual machine installation and use
  • 2. Linux basics
  • 3. Remote control
  • 4. Multi-machine communication configuration
  • 5. Static IP and hotspot mode
  • 6. Bind the device ID
  • 7. Web page real-time monitoring
  • 8. Expansion tutorial
  • 9.1 Jetson Nano write sytem
  • 9.2 Raspberry Pi write system
  • 9.3 Orin series write system
  • 10.Jetpack old system upgraded to SUPER systemnew
21.ROS2- Docker(Nano Pi)
  • 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. Entering the Docker Container of the Robot
  • 6. Building a Robot Development Environment in Docker
  • 7. Method for customers to update 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- Depth camera course
  • 1.Depth camera usage
  • 2. Camera internal reference 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
25.ROS2- Lidar course
  • 1. Introduction and Use of Lidar
  • 2. Lidar avoiding
  • 3. Lidar follow
  • 4. Lidar guard
  • 5. Patrol game
  • 6. Gmapping mapping algorithm
  • 7. Cartographer mapping algorithm
  • 8. Navigation and obstacle avoidance
  • 9. Rosmaster_ X3 conducts mapping through the APP
  • 10. Rosmaster_ X3 Navigation through APP
26.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、line patrol automatic driving
27.ROS2- Voice control courses
  • 0.How to use old voice module in new image
  • 1. Introduction of voice module-new
  • 2. Voice control module port binding-new
  • 1. Introduction of voice module and port binding-old
  • 3. Voice control car movement
  • 4. Voice control Autopilot
  • 5. Voice control color recognition
  • 6. Voice control color tracking
  • 7. 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, ROSMASTER X3 cannot start normally
Download
  • RemoteCtrl_APP(Android/iOS)
  • Mapping_APP(iOS_Android)
  • Instruction Manual
  • SystemFile_ROS2
  • SystemFile_ROS1
  • SystemFile_ROS2_OrinNANOSUPER
  • SystemFile_ROS2_OrinNXSUPER
  • Code
  • Jetson_Boot_USB.tar.gz
  • VM software
  • Autopilot Annex
  • Other_Annex_File
  • LLM_SystemFile
  • ROS2_Basic_VM
Download ZIP
FAQ
  • 1、How to connect OLED?

Welcome to ROSMASTER X3 repository

12.6 gmapping mapping algorithm

6. gmapping mapping algorithm

6. gmapping mapping algorithm6.1. Introduction6.2. Use6.2.1. Start6.2.2. Controlling the robot6.2.3. Map saving6.2. Topics and services6.3. Configuration parameters6.4, TF transformation

Gmapping: http://wiki.ros.org/gmapping/

map_server: https://wiki.ros.org/map_server

6.1. Introduction

  • gmapping is only applicable to points where the number of two-dimensional laser points in a single frame is less than 1440. If the number of laser points in a single frame is greater than 1440, then problems such as [[mapping-4] process has died] will occur.

  • Gmapping is a commonly used open source SLAM algorithm based on the filtered SLAM framework.

  • Gmapping is based on the RBpf particle filter algorithm, which separates the real-time positioning and mapping processes. Positioning is performed first and then mapping is performed.

  • Gmapping has made two major improvements on the RBpf algorithm: improved proposal distribution and selective resampling.

Advantages:Gmapping can construct indoor maps in real time. The amount of calculation required to construct small scene maps is small and the accuracy is high.

Disadvantages: As the scene grows, the number of particles required increases because each particle carries a map, so the amount of memory and computation required when building a large map increases. Therefore it is not suitable for building large scene maps. And there is no loop detection, so the map may be misaligned when the loop is closed. Although increasing the number of particles can close the map, it comes at the expense of increased calculations and memory.

image-20220224141102272

6.2. Use

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

According to different models, you only need to set the purchased model in [.bashrc], X1 (normal four-wheel drive) X3 (Mailun) Take X3 as an example

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#Raspberry Pi 5 master needs to enter docker first, please perform this step
#If running the script into docker fails, please refer to ROS/07, Docker tutorial
~/run_docker.sh

Open the [.bashrc] file

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sudo vim .bashrc

Find the [ROBOT_TYPE] parameters and modify the corresponding car model

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export ROBOT_TYPE=X3 # ROBOT_TYPE: X1 X3 X3plus R2 X7

6.2.1. Start

Start the command (robot side). For the convenience of operation, this section takes [mono + laser + yahboomcar] as an example.

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roslaunch yahboomcar_nav laser_bringup.launch # laser + yahboomcar
roslaunch yahboomcar_nav laser_usb_bringup.launch # mono + laser + yahboomcar
roslaunch yahboomcar_nav laser_astrapro_bringup.launch # Astra + laser + yahboomcar

<PI5 needs to open another terminal to enter the same docker container

image-20240408144126098

Mapping command (robot side)

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roslaunch yahboomcar_nav yahboomcar_map.launch use_rviz:=false map_type:=gmapping
  • [use_rviz] parameter: whether to enable rviz visualization.

  • [map_type] parameter: Set the mapping algorithm [gmapping].

Turn on the visual interface (virtual machine side)

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roslaunch yahboomcar_nav view_map.launch

image-20220224143306896

The gap at the back of the robot is due to the obstruction caused by the installation position of the display screen, so a certain range of radar data is blocked. The shielding range can be adjusted, or it can not be blocked according to the actual situation. For specific operations, see [01. Radar Basic Course].

6.2.2. Controlling the robot

  • Keyboard controls robot movement

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rosrun teleop_twist_keyboard teleop_twist_keyboard.py # System integration
roslaunch yahboomcar_ctrl yahboom_keyboard.launch # Custom
  • Control the robot movement with the handle

Make the robot cover the area to be mapped and the map should be as closed as possible.

There may be some scattered points during the mapping process. If the mapping environment is well closed, relatively regular, and the movement is slow, the scattering phenomenon will be much smaller.

6.2.3. Map saving

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rosrun map_server map_saver -f ~/yahboomcar_ws/src/yahboomcar_nav/maps/my_map # The first way
bash ~/yahboomcar_ws/src/yahboomcar_nav/maps/map.sh # The second way

The map will be saved to the ~/yahboomcar_ws/src/yahboomcar_nav/maps/ folder, a pgm image and a yaml file.

map.yaml

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image: map.pgm
resolution: 0.05
origin: [-15.4,-12.2,0.0]
Negate: 0
occupied_thresh: 0.65
free_thresh: 0.196

Parameter analysis:

  • image: The path of the map file, which can be an absolute path or a relative path.

  • resolution: resolution of the map, meters/pixel

  • Origin: 2D pose (x, y, yaw) in the lower left corner of the map. The yaw here is rotated counterclockwise (yaw=0 means no rotation). Many parts of the current system ignore the yaw value.

  • negate: whether to reverse the meaning of white/black and free/occupied (the interpretation of the threshold is not affected)

  • occupied_thresh: Pixels with an occupation probability greater than this threshold will be considered fully occupied.

  • free_thresh: Pixels with occupancy probability less than this threshold will be considered completely free.

6.2. Topics and services

Subscription topicTypeDescription
tftf/tfMessageUsed for conversion between lidar coordinate system, base coordinate system, and odometer coordinate system
scansensor_msgs/LaserScanLidar scan data
Post TopicTypeDescription
map_metadatanav_msgs/MapMetaDataPublish map Metadata
mapnav_msgs/OccupancyGridPublish map raster data
~entropystd_msgs/Float64Publish an estimate of the entropy of the robot pose distribution
ServiceTypeDescription
dynamic_mapnav_msgs/GetMapGet map data

Node view

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rqt_graph

image-20220224141102272

6.3. Configuration parameters

ParametersTypeDefault valueDescription
~throttle_scansint1Each time this number of frames of laser data is received, only one frame of data is processed. By default, each frame of data is processed
~base_framestring"base_link"Robot base coordinate system
~map_framestring"map"Map coordinate system
~odom_framestring"odom"Odometer coordinate system
~map_update_intervalfloat5.0Map update frequency, the lower the value, the greater the computational load
~maxUrangefloat80.0The maximum range that the laser can detect
~sigmafloat0.05standard deviation of endpoint matches
~kernelSizeint1Search in the corresponding kernel
~lstepfloat0.05Optimization step size during translation
~astepfloat0.05Optimization step size during rotation
~iterationsint5Number of iterations to scan for matches
~lsigmafloat0.075Laser standard deviation of likelihood calculation
~ogainfloat3.0Used to smooth the resampling effect during likelihood calculation
~lskipint0Number of beams skipped per scan
~minimumScorefloat0The minimum value of scan matching results. When using a laser scanner with a limited range (e.g. 5m), jumping in large open spaces can be avoided
~srrfloat0.1Translation function (rho/rho), odometry error during translation
~srtfloat0.2rotation function (rho/theta), odometry error in translation
~strfloat0.1Translation function (theta/rho), odometry error when rotating
~sttfloat0.2Rotation function (theta/theta), odometry error when rotating
~linearUpdatefloat1.0Process the laser scanning data every time the robot translates this distance
~angularUpdatefloat0.5The robot processes laser scanning data every time it rotates this distance
~temporalUpdatefloat-1.0Process a scan if the latest scan is processed slower than the update. Turn off time-based updates when this value is negative
~resampleThresholdfloat0.5Neff-based resampling threshold
~particlesint30Number of particles in the filter
~xminfloat-100.0Initial minimum size of map in x direction
~yminfloat-100.0The initial minimum size of the map in the y direction
~xmaxfloat100.0The initial maximum size of the map in the x direction
~ymaxfloat100.0The initial maximum size of the map in the y direction
~deltafloat0.05map resolution
~llsamplerangefloat0.01Translation sampling distance for likelihood calculation
~llsamplestepfloat0.01translation sampling step for likelihood calculation
~lasamplerangefloat0.005Rotated sampling distance for likelihood calculation
~lasamplestepfloat0.005Rotation sampling step size for likelihood calculation
~transform_publish_periodfloat0.05TF transform publishing time interval
~occ_threhfloat0.25Threshold for raster map occupancy
~maxRange(float)float-The maximum range of the sensor

6.4, TF transformation

Required TF transformationDescription
laser-->base_linkThe transformation between the laser radar coordinate system and the base coordinate system is generally published by robot_state_publisher or static_transform_publisher
base_link-->odomTransformation between the map coordinate system and the robot odometer coordinate system, estimating the robot's pose in the map
Released TF TransformDescription
map-->odomTransformation between the map coordinate system and the robot's odometry coordinate system, estimating the robot's pose in the map

View tf tree

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rosrun rqt_tf_tree rqt_tf_tree

image-20220224141102272

Patrol game
hector mapping algorithm

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