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ROSMASTER M1📖
01.Robot Configuration and Operation Guide
  • 0. Assembly video
  • 1. Servo PTZ calibration
  • 2. Quick start guide handle control
  • 3. First trial
  • 4. Enter Docker (For JETSON Nano and RPi 5)
  • 1.5 Quick Start video
02.AI Model Preparation
  • AI Model Preparation
03.AI Model Basics course
  • 1.AI large model types and principles
  • 2.RAG retrieval enhancement and model training samples
  • 3.Embodied intelligent robot system architecture
  • 4.Embodied intelligent functions core source code
04.AI Model Developmentnew
  • 1.Register a model service provider account
  • 2. Configuring API-KEY
  • 3. Introduction to Dify
  • 4. Basic Dify Features
  • 5. Deploy the RAG knowledge base
  • 6. AI Large Model Development - chatbot
  • 7. RAG knowledge base + chatbot
  • 8. AI agent workflow
  • 9. Custom wake-up response
  • 10. Core module testing tools
05.AI Model - Text Version
  • 1.Semantic understand and command follow
  • 2.Multimodal visual understand
  • 3.Multimodal visual understand + PTZ tracking
  • 4.Multimodal visual understand + visual following
  • 5.Multimodal visual understand + visual line patroling
  • 6.Multimodal visual understand + Depth Camera Distance Question Answering
  • 7.Multimodal visual understand+SLAM navigation
  • 8.Multimodal visual understand+SLAM navigation + visual line patroling
  • 9.Intention estimation + Multimodal visual understand + SLAM navigation + visual functions
06.AI Model - Voice Version
  • 1.Semantic understand and command follow
  • 2.Multimodal visual understand
  • 3.Multimodal visual understand + PTZ tracking
  • 4.Multimodal visual understand + visual following
  • 5.Multimodal visual understand + visual line patroling
  • 6.Multimodal visual understand + Depth Camera Distance Question Answering
  • 7.Multimodal visual understand+SLAM navigation
  • 8.Multimodal visual understand+SLAM navigation + visual line patroling
  • 9.Intention estimation + Multimodal visual understand + SLAM navigation + visual functions
07.Chassis Control Course
  • 1. ROS control
  • 2. Handle control
  • 3. Keyboard control
  • 4. Linear velocity calibration
  • 5. Angular velocity calibration
  • 6. URDF model
08.Mediapipe Visual Course
  • 1.Hand detection
  • 2.Posture detection
  • 3.Overall detection
  • 4.Facial landmark detection
  • 5.Face detection
  • 6.Face special effects
  • 7.3D object recognition
  • 8.Brush
  • 9.Finger control
  • 10.Gesture recognition
  • 11.Palm target positioning
  • 12.Fingertip trajectory recognition
09.Advanced Vision Course
  • 1.QR code
  • 2.Human Pose Estimation
  • 3.Object Detection
  • 4.AR Vision
  • 5.ROS+Opencv Basics
  • 6.Yolov8 Object Detection (ORIN)
  • 7.Deep Learning Object Tracking (ORIN)
10.HD Camera Vision Course(Standard Version)
  • 01.Color tracking
  • 02.Face Tracking
  • 03.AprilTag machine code tracking
  • 04.KCF Object Tracking
  • 05.QR code tracking
  • 06.Mediapipe gesture tracking
  • 07.Meidpipe human posture tracking
  • 08.Color Follow
  • 09.Face Following
  • 10.AprilTag machine code following
  • 11.QR code follow
  • 12.Mediapipe gesture recognition and following
  • 13.Meidpipe human posture following
  • 14.Visual tracking autonomous driving
  • 15.QR code motion control
  • 16.Gesture motion control
11.Depth Camera Vision Course(Superior Version)
  • 1.Basic use of depth camera
  • 2.Deep pseudo-color image
  • 3.Depth camera ranging
  • 4.Color block volume measurement
  • 5.Color Following
  • 6.Face Following
  • 7.QR code follow
  • 8.Machine code following
  • 9.KCF Object Following
  • 10.Mediapipe gesture following
  • 11.Mediapipe Posture Following
  • 12.Visual tracking autonomous driving
  • 13.Edge Detection
12.LiDAR Course
  • 1.Basic use of Lidar
  • 2.Lidar obstacle avoidance
  • 3.Lidar following
  • 4.Lidar patrol
  • 5.Gmapping-SLAM mapping
  • 6.Cartographer-SLAM mapping
  • 7.slam_toolbox mapping
  • 8.Navigation2 single-point navigation avoid
  • 9.Navigation2 multi-point navigation avoid
  • 10.Cartographer Repositioning navigation
  • 11.RTAB-Map mapping
  • 12.RTAB-Map navigation
  • 13.APP mapping navigation
13.Multi-Vehicle Course
  • 1.Multi-vehicle control
  • 2.Multi-vehicle navigation
  • 3.Multi-vehicle platoon
14.Autonomous Driving expand(Need purchase track map)
【Track Map】Multimodal visual understand+SLAM navigation
  • 1. Multimodal visual understand+SLAM navigation
【Track Map】Autonomous driving (RDKX5 & Orin)
  • 1. Venue placement and precautions
  • 2. Start testing
  • 3. Identify and debug
  • 4. Lane keeping
  • 5. Road sign detection
  • 6. Traffic light recognition
  • 7. Fixed point parking
  • 8. Steering decision
  • 9. Horn sounding
  • 10. Deceleration
  • 11. Speed limit
  • 12. Autonomous parking
  • 13. Comprehensive application
  • 14. Unmanned driving integrated AI large model
【Track Map】Autonomous driving (Jetson Nano)
  • 1. Venue placement and precautions
  • 2. Start testing
  • 3. Identify and debug
  • 4. Lane keeping
  • 5. Road sign detection
  • 6. Traffic light recognition
  • 7. Fixed point parking
  • 8. Steering decision
  • 9. Horn sounding
  • 10. Deceleration
  • 11. Speed limit
  • 12. Autonomous parking
  • 13. Comprehensive application
【Track Map】Road network planning
  • 1. Introduction to Road Network Planning and Navigation
  • 2. Constructing a Pose Map
  • 3. Road Network Labelling
  • 4. Road Network Planning Combined with Sand Table Map
  • 5. Path Replanning
【Track Map】Model Training
  • 1. Data Collection
  • 2. Dataset Annotation
  • 3. YOLOv11 Model Training
  • 4. Model Format Conversion
15.YOLOV11 development course (ORIN)
  • 1.YOLO environment construction
  • 2.CLI use
  • 3.Object detection
  • 4.Instance segmentation
  • 5.Pose estimation
  • 6.Image classification
  • 7.Oriented object detection
  • 8.Model conversion
  • 9.Dataset annotation
  • 10.Model Training_Conversion
  • 11.Model prediction
16.YOLOV11 development course(RPi&Jetson NANO)
  • 1.YOLO environment construction
  • 2.CLI use
  • 3.Object detection
  • 4.Instance segmentation
  • 5.Pose estimation
  • 6.Image classification
  • 7.Oriented object detection
  • 8.Model conversion
  • 9.Model prediction
17.YOLOV11 development course(RDK X5)
  • 1.YOLO environment construction
  • 2.CLI use
  • 3.Object detection
  • 4.Instance segmentation
  • 5.Pose estimation
  • 6.Image classification
  • 7.Oriented object detection
  • 8.Model prediction
  • 9.Model Quantization
18.Main control board course
Jetson Nano B01
  • 1. Introduction to the Jetson Nano B01 SUB Board
  • 2. Burn the system image
  • 3. Re-read the memory card or USB drive that has been burned
  • 4. Startup system
  • 5. Jetson Nano B01 System and Desktop Introduction
  • 6. Memory card or USB flash drive expansion
  • 7. Network Configuration
  • 8. SSH remote login & file transfer
  • 9. VNC Remote Login
  • 10. Jetson Nano B01 system backup
  • 11. Jetson Nano Swap space increased
  • 12. Installation and use of Jtop
  • 13. Third-party board + USB drive to start the car
Jetson Orin Nano_NX
  • 1.Jetson Orin board introduction
  • 2.Restore the factory image system
  • 3.Backup SSD system
  • 4.SSD expansion
  • 5.Network configuration
  • 6.SSH remote login
  • 7.VNC remote control
  • 8.Remote file transfer
  • 9.Jtop tool
  • 10.Exchange space expansion
Raspberry Pi
  • 1. Introduction to Raspberry PI 5
  • 2. Raspberry PI system installation and backup
  • 3. Powering the Raspberry PI 5
  • 4. Startup of Raspberry PI 5
  • 5. Update and upgrade operating system
  • 6. Introduction to raspi-config tool
  • 7. config.txt file description
  • 8. Network Configuration
  • 9. remote access
  • 10. Transfer files remotely
  • 11. Set display resolution and rotation
  • 12. Set screen to sleep
  • 13. Play audio and video
  • 14. Using USB camera
  • 15. Using MIPI camera
  • 16. Get real-time temperature of Raspberry Pi
RDK X5
  • 1.Introduction to RDK X5 board
  • 2.Write system file
  • 3.Hardware interface description
  • 4.Install case and other accessories
  • 5.Network configuration
  • 6.Remote login
  • 7.Update system
  • 8.srpi-config user guide
  • 9.config. txt configuration file
  • 10.Self-startup setting
  • 11.RDK exclusive command usage
  • 12.Linux command usage
  • 13.GPIO readwrite operation
  • 14.Image classification algorithm
  • 15.Use USB camera
  • 16.Use MIPI camera
  • 17.Binocular camera depth algorithm
19.AI large model course(ORIN-Pi-RDK X5)
Offline large model (Text Interaction)
  • 0. AI Large Model Mirroring Explanation
  • 1. Deployment of large-scale AI models
  • 2.Installation of the large-scale model dialogue platform
  • 3. Meta AI Llama 3.2 model
  • 4. Alibaba Cloud Qwen3 Model
  • 5. Microsoft Phi-4 mini model
  • 6. DeepSeek DeepSeek-R1 model
  • 7. Alibaba Cloud Qwen2.5vl Visual Multimodal Large Model
  • 8. Google Gemma3 Visual Multimodal Large Model
  • 9. Llava Visual Multimodal Large Model
  • 10.MiniCPM-V Visual Multimodal Large Model
  • 11. Multimodal Visual Understanding Applications
  • 12. Multimodal Text-Based Image Application
  • 13. Multimodal Video Analysis Application
  • 14. Multimodal visual positioning applications
  • 15. Multimodal table scanning application
  • 16. Multimodal Autonomous Agent Application
Offline large model (Voice Interaction)
  • 0. Voice Interaction Hardware Connection (Must Read)
  • 1. Offline speech-to-text (ASR)
  • 2. Offline Text-to-Speech (TTS)
  • 3. AI large model voice interaction
  • 4. Multimodal visual understanding and voice interaction
  • 5. Multimodal Text-Based Image Application
  • 6. Multimodal Video Analysis Application
  • 7. Multimodal visual positioning applications
  • 8. Multimodal table scanning application
  • 9. Multimodal Autonomous Agent Applications
  • 10. AI large model offline voice assistant
Online large model (Text Interaction)
  • 1. OpenRouter Large Model API Aggregation Platform
  • 2. Multimodal visual understanding applications
  • 3. Multimodal visual positioning applications
  • 4. Multimodal table scanning application
  • 5. Multimodal Autonomous Agent Applications
Online large model (Voice Interaction)
  • 0. Voice Interaction Hardware Connection (Must Read)
  • 1. Online speech-to-text (ASR)
  • 2. Online Text-to-Speech (TTS)
  • 3. AI large model voice interaction
  • 4. Multimodal visual and voice interaction
  • 5. Multimodal visual positioning applications
  • 6. Multimodal table scanning application
  • 7. Multimodal Autonomous Agent Applications
  • 8. AI large model online voice assistant
20.AI large model course(Jetson NANO)
Offline large model (Text Interaction)
  • 4.Multimodal visual understand speech interaction
  • 0.AI large model system image instructions
  • 1.AI large model environment deployment
  • 2.Install large model dialogue platform
  • 3.Meta AI llama 3.2 model
  • 4.Alibaba Cloud Qwen2 model
  • 5.Alibaba Cloud Qwen3 model
  • 6.SUTD TinyLlama
  • 7.DeepSeek DeepSeek-R1 model
  • 8.Microsoft Phi-3
  • 9.Microsoft Orca Mini
  • 10.NVIDIA StarCoder2
  • 11.Google Gemma3 Visual Multimodal Large Model
  • 12.Offline Text to Speech (TTS)
  • 13.Offline Speech to Text (ASR)
Online large model (Text Interaction)
  • 1.OpenRouter large model API aggregation platform
  • 2.Multimodal visual understand application
  • 3.Multimodal visual localization application
  • 4.Multimodal table scanning application
  • 5.Multimodal autonomous proxy application
Online large model (Voice Interaction)
  • 0.Voice interaction hardware connection (ReadMe)
  • 1.Online speech-to-text (ASR)
  • 2.Online Text-to-Speech (TTS)
  • 3.AI large model voice interaction
  • 5.Multimodal visual position application
  • 6.Multimodal table scanning application
  • 7.Multimodal autonomous proxy application
  • 8.AI large model online voice assistant
21.Linux operating system course
  • 1.Introduction to Linux system
  • 2.Ubuntu file system
  • 3.Ubuntu common commands
  • 4.Ubuntu common editors
  • 5.Ubuntu software operation commands
  • 6.Virtual machine installation
  • 7.SSH remote control
  • 8.VNC remote control
  • 9.Transfer files remotely
  • 10.Driver library and communication
  • 11.Static IP and hotspot mode
  • 12.Bind device ID
  • 13.Capacity expansion and resource allocation
  • 14.Update system software sources
  • 15.Set root user password
  • 16.sudo free password
  • 17.Connect to WiFi network
  • 18.View system version
  • 19.Customized service management
  • 20.Back up system image
22.Docker course
  • 1.Docker Introduction and Installation
  • 2.Docker basic commands
  • 3.Docker Submission and Multi-Terminal Access
  • 4.Docker Startup Script
  • 5.Docker-Vscode environment construction
23.ROS2 basic course
  • 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
24.Image processing course
  • 1. Introduction to Open Source CV
  • 2. Image reading and display
  • 3. Image writing
  • 4. Image quality
  • 5. Pixel Operation
  • 6. Image zoom
  • 7. Image cutting
  • 8. Image translation
  • 9. Image Mirroring
  • 10. Affine Transformation
  • 11. Image Rotation
  • 12. Perspective Transformation
  • 13. Grayscale processing
  • 14. Image Binarization
  • 15. Edge Detection
  • 16. Line drawing
  • 17. Rectangle and circle drawing
  • 18. Drawing text and pictures
25.STM32 driver course
  • 0. Notes (must read)
  • 1. About expansion board
  • 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
26.Main control communication course
  • 1. Update the expansion board firmware
  • 2. Install Rosmaster driver library
  • 3. Jupyter lab basic usage tutorial
  • 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
Download
  • Mapping_APP(iOS_Android)
  • Robot_Image_System_File
  • Code-Firmware
  • Instruction_Manual
  • MainBoard_AI_Pure_System
  • VM_File
  • Software_Tools
  • Hardware_Info
  • 3D_Model_File
Download ZIP

Welcome to ROSMASTER M1 repository

12.RTAB-Map navigation

RTAB-Map Navigation

1. Content Description

This section explains how to implement RTAB-Map navigation by combining the robot chassis, LiDAR, depth camera, and Navigation2.

This section requires entering commands in the terminal. This lesson uses the Raspberry Pi as an example.

For Raspberry Pi and Jetson-Nano boards, you need to open a terminal on the host computer and enter the command to enter the Docker container. Once inside the Docker container, enter the commands mentioned in this lesson in the terminal. For instructions on entering the Docker container from the host computer, refer to [01. Robot Configuration and Operation Guide] -- [4.Enter Docker (For JETSON Nano and RPi 5)].

For RDKX5 and Orin boards, simply open a terminal and enter the commands mentioned in this lesson.

2. Preparation

Due to performance limitations, the Raspberry Pi 5 and Jetson Nano cannot smoothly run the RTAB-Map algorithm in Docker on the board. Therefore, a virtual machine is required to facilitate this. To enable distributed communication between the car and the virtual machine, two steps are required:

  • Both must be on the same local area network. This is most easily achieved by connecting to the same Wi-Fi network.
  • Both devices must have the same ROS_DOMAIN_ID. The default ROS_DOMAIN_ID for the car is 30, and the default ROS_DOMAIN_ID for the virtual machine is also 30. If they are different, modify the virtual machine's ROS_DOMAIN_ID. To do this, modify the ~/.bashrc file and change the ROS_DOMAIN_ID value to match the car's. Save and exit the file, then enter the command source ~/.bashrc to refresh the environment variables.
  • To verify distributed communication between the two, run the driver program on the board, then enter ros2 node list on the virtual machine. If the underlying data topic appears, the two devices are communicating.

The Orin board can be run directly on the board.

3. Program Startup

For Raspberry Pi and Jetson-Nano boards, you need to enter the Docker container first. For RDKX5 and Orin controllers, this is not necessary.

Enter the Docker container (see [Docker course] --- [4. Docker Startup Script] for steps).

All the following commands must be executed within the same Docker container (see [Docker course] --- [3. Docker Submission and Multi-Terminal Access] for steps).

First, enter the following command in the car terminal to start the depth camera.

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ros2 launch ascamera hp60c.launch.py

image-20250813192335870

Enter the following command in the virtual machine terminal for rviz visualization.

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ros2 launch yahboomcar_nav display_rtabmap_nav_launch.py

image-20250813192514663

Wait for RTAB navigation to start.

Enter the following command in the terminal to start the chassis and mapping.

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ros2 launch yahboomcar_nav navigation_rtabmap_launch.py

image-20250813192630529

At the same time, rviz will display the map.

image-20250813192653559

  • From rviz, you can see that the robot automatically estimates its initial position, eliminating the need for manual position initialization.
  • If the car's initial position is significantly off, use the [2D Pose Estimate] tool in the RVIZ toolbar to provide an approximate position for quick positioning.

image-20250813192801888

Enter the following command in the terminal to start navigation2:

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ros2 launch yahboomcar_nav rtabmap_nav_launch.py

After everything is successfully launched, an expansion area will appear, as shown below.

image-20250813192935992

Then use the [Nav2 Goal] tool in RVIZ to specify a target point for the car, and it will navigate to that point.

image-20250813193031495

Note: If navigation performance is poor, consider building a map in a closed environment. This is because the robot relies on deep visual odometry, and the robot's straight line is not very straight. When the robot moves forward or turns, the deep visual odometry will deviate. This will eventually lead to accumulated errors and drift. A closed environment will provide better performance, or you can increase the mapping time to record more points for each feature point on the map.

4. View the Node Communication Graph

In the terminal, enter:

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ros2 run rqt_graph rqt_graph

If the map does not display initially, select [Nodes/Topics (all)] and click the refresh button in the upper left corner.

rosgraph

5. Node Details for rtabmap Navigation

View RGB Image Synchronization Node Information

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ros2 node info /rgbd_sync

image-20250813193816116

View the /rtabmap node

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ros2 node info /rtabmap
​x
/rtabmap
  Subscribers:
    /global_pose: geometry_msgs/msg/PoseWithCovarianceStamped
    /goal: geometry_msgs/msg/PoseStamped
    /goal_node: rtabmap_msgs/msg/Goal
    /gps/fix: sensor_msgs/msg/NavSatFix
    /imu: sensor_msgs/msg/Imu
    /initialpose: geometry_msgs/msg/PoseWithCovarianceStamped
    /landmark_detection: rtabmap_msgs/msg/LandmarkDetection
    /landmark_detections: rtabmap_msgs/msg/LandmarkDetections
    /parameter_events: rcl_interfaces/msg/ParameterEvent
    /rgbd_image: rtabmap_msgs/msg/RGBDImage
    /rtabmap/republish_node_data: std_msgs/msg/Int32MultiArray
    /scan: sensor_msgs/msg/LaserScan
    /user_data_async: rtabmap_msgs/msg/UserData
  Publishers:
    /cloud_ground: sensor_msgs/msg/PointCloud2
    /cloud_map: sensor_msgs/msg/PointCloud2
    /cloud_obstacles: sensor_msgs/msg/PointCloud2
    /diagnostics: diagnostic_msgs/msg/DiagnosticArray
    /global_path: nav_msgs/msg/Path
    /global_path_nodes: rtabmap_msgs/msg/Path
    /goal_out: geometry_msgs/msg/PoseStamped
    /goal_reached: std_msgs/msg/Bool
    /grid_prob_map: nav_msgs/msg/OccupancyGrid
    /info: rtabmap_msgs/msg/Info
    /labels: visualization_msgs/msg/MarkerArray
    /landmarks: geometry_msgs/msg/PoseArray
    /local_grid_empty: sensor_msgs/msg/PointCloud2
    /local_grid_ground: sensor_msgs/msg/PointCloud2
    /local_grid_obstacle: sensor_msgs/msg/PointCloud2
    /local_path: nav_msgs/msg/Path
    /local_path_nodes: rtabmap_msgs/msg/Path
    /localization_pose: geometry_msgs/msg/PoseWithCovarianceStamped
    /map: nav_msgs/msg/OccupancyGrid
    /mapData: rtabmap_msgs/msg/MapData
    /mapGraph: rtabmap_msgs/msg/MapGraph
    /mapOdomCache: rtabmap_msgs/msg/MapGraph
    /mapPath: nav_msgs/msg/Path
    /octomap_binary: octomap_msgs/msg/Octomap
    /octomap_empty_space: sensor_msgs/msg/PointCloud2
    /octomap_full: octomap_msgs/msg/Octomap
    /octomap_global_frontier_space: sensor_msgs/msg/PointCloud2
    /octomap_grid: nav_msgs/msg/OccupancyGrid
    /octomap_ground: sensor_msgs/msg/PointCloud2
    /octomap_obstacles: sensor_msgs/msg/PointCloud2
    /octomap_occupied_space: sensor_msgs/msg/PointCloud2
    /parameter_events: rcl_interfaces/msg/ParameterEvent
    /rosout: rcl_interfaces/msg/Log
    /tf: tf2_msgs/msg/TFMessage
  Service Servers:
    /rtabmap/add_link: rtabmap_msgs/srv/AddLink
    /rtabmap/backup: std_srvs/srv/Empty
    /rtabmap/cancel_goal: std_srvs/srv/Empty
    /rtabmap/cleanup_local_grids: rtabmap_msgs/srv/CleanupLocalGrids
    /rtabmap/describe_parameters: rcl_interfaces/srv/DescribeParameters
    /rtabmap/detect_more_loop_closures: rtabmap_msgs/srv/DetectMoreLoopClosures
    /rtabmap/get_map: nav_msgs/srv/GetMap
    /rtabmap/get_map_data: rtabmap_msgs/srv/GetMap
    /rtabmap/get_map_data2: rtabmap_msgs/srv/GetMap2
    /rtabmap/get_node_data: rtabmap_msgs/srv/GetNodeData
    /rtabmap/get_nodes_in_radius: rtabmap_msgs/srv/GetNodesInRadius
    /rtabmap/get_parameter_types: rcl_interfaces/srv/GetParameterTypes
    /rtabmap/get_parameters: rcl_interfaces/srv/GetParameters
    /rtabmap/get_plan: nav_msgs/srv/GetPlan
    /rtabmap/get_plan_nodes: rtabmap_msgs/srv/GetPlan
    /rtabmap/get_prob_map: nav_msgs/srv/GetMap
    /rtabmap/global_bundle_adjustment: rtabmap_msgs/srv/GlobalBundleAdjustment
    /rtabmap/list_labels: rtabmap_msgs/srv/ListLabels
    /rtabmap/list_parameters: rcl_interfaces/srv/ListParameters
    /rtabmap/load_database: rtabmap_msgs/srv/LoadDatabase
    /rtabmap/log_debug: std_srvs/srv/Empty
    /rtabmap/log_error: std_srvs/srv/Empty
    /rtabmap/log_info: std_srvs/srv/Empty
    /rtabmap/log_warning: std_srvs/srv/Empty
    /rtabmap/octomap_binary: octomap_msgs/srv/GetOctomap
    /rtabmap/octomap_full: octomap_msgs/srv/GetOctomap
    /rtabmap/pause: std_srvs/srv/Empty
    /rtabmap/publish_map: rtabmap_msgs/srv/PublishMap
    /rtabmap/remove_label: rtabmap_msgs/srv/RemoveLabel
    /rtabmap/reset: std_srvs/srv/Empty
    /rtabmap/resume: std_srvs/srv/Empty
    /rtabmap/set_goal: rtabmap_msgs/srv/SetGoal
    /rtabmap/set_label: rtabmap_msgs/srv/SetLabel
    /rtabmap/set_mode_localization: std_srvs/srv/Empty
    /rtabmap/set_mode_mapping: std_srvs/srv/Empty
    /rtabmap/set_parameters: rcl_interfaces/srv/SetParameters
    /rtabmap/set_parameters_atomically: rcl_interfaces/srv/SetParametersAtomically
    /rtabmap/trigger_new_map: std_srvs/srv/Empty
    /rtabmap/update_parameters: std_srvs/srv/Empty
  Service Clients:
    /rtabmap/describe_parameters: rcl_interfaces/srv/DescribeParameters
    /rtabmap/get_parameter_types: rcl_interfaces/srv/GetParameterTypes
    /rtabmap/get_parameters: rcl_interfaces/srv/GetParameters
    /rtabmap/list_parameters: rcl_interfaces/srv/ListParameters
    /rtabmap/set_parameters: rcl_interfaces/srv/SetParameters
    /rtabmap/set_parameters_atomically: rcl_interfaces/srv/SetParametersAtomically
  Action Servers:
​
  Action Clients:
RTAB-Map mapping
APP mapping navigation

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