Advanced SLAM with ROS 2: Sample Codes on Yahboom Jetson Orin Nano Robot

Introduction to SLAM with ROS 2

Simultaneous Localization and Mapping (SLAM) has become a fundamental component in modern robotics, enabling autonomous navigation and environmental understanding. With ROS 2 (Robot Operating System 2), SLAM implementations are more robust and flexible, supporting multi-threading, Quality of Service (QoS) profiles, and real-time communication. These ROS 2 improvements are particularly beneficial when working with advanced sensors and SLAM on platforms like the Yahboom Jetson Orin Nano 4G Robot.

This Yahboom robot is equipped with a high-performance NVIDIA Jetson Orin Nano, capable of handling compute-intensive tasks such as 3D mapping and real-time localization with lidar. Leveraging ROS 2’s SLAM packages—like Cartographer and RTAB-Map—users can achieve accurate and efficient mapping in diverse environments.

In this guide, we’ll cover how to set up and code advanced SLAM on the Yahboom Jetson Orin Nano Robot using ROS 2, followed by optimization techniques and code samples. By implementing SLAM on this Yahboom robot, enthusiasts can test their coding skills, perform real-world SLAM experiments, and get hands-on experience with one of the leading-edge robotics platforms.

Setting Up Yahboom Jetson Orin Nano for ROS 2 SLAM

The first step to implement SLAM with ROS 2 on the Yahboom Jetson Orin Nano Robot involves configuring ROS 2 and setting up compatible SLAM packages. Yahboom’s hardware includes lidar, IMU, and depth sensors, all of which are crucial for efficient SLAM processing.

1. **Install ROS 2 (Humble or Foxy)**:
ROS 2 Humble is ideal for compatibility with Yahboom’s hardware. Begin with:
sudo apt update && sudo apt install ros-humble-desktop
After installation, source the workspace:
source /opt/ros/humble/setup.bash

2. **Set Up the ROS 2 Workspace**:
Create a workspace to manage SLAM packages and custom nodes:
mkdir -p ~/ros2_ws/src
cd ~/ros2_ws
colcon build
Source the workspace:
source install/setup.bash

3. **Install SLAM Packages**:
The Yahboom Jetson Orin Nano robot supports Cartographer and RTAB-Map. Install both for 2D and 3D SLAM:
sudo apt install ros-humble-cartographer-ros ros-humble-rtabmap-ros
Cartographer handles 2D lidar data, while RTAB-Map processes 3D SLAM with RGB-D and lidar.

4. **Configure Lidar and Sensor Connections**:
The robot’s lidar sensor requires a specific data rate, so configure ROS 2 Quality of Service (QoS) profiles for accurate data streaming. In the SLAM configuration files, adjust parameters like depth and QoS profile type to match the lidar’s specifications. Proper QoS settings ensure smoother data processing.

With the Yahboom Jetson Orin Nano set up, the robot is ready to perform advanced SLAM tasks, starting with sample code for basic mapping and localization.

Sample Code for SLAM with Lidar in ROS 2

With the Yahboom Jetson Orin Nano set up for ROS 2, we’ll now dive into SLAM implementation. This sample code will initialize lidar data collection and visualize mapping output. We’ll use Cartographer for 2D SLAM and RTAB-Map for 3D SLAM.

1. **Initializing Lidar for Cartographer SLAM**:
Start with a launch file to initialize Cartographer using lidar data:
ros2 launch cartographer_ros cartographer.launch.py use_lidar:=true
This command starts Cartographer in lidar mode, building a 2D map based on lidar input. Confirm lidar data flow by echoing the topic:
ros2 topic echo /scan

2. **Configuring RTAB-Map for 3D SLAM**:
RTAB-Map enables 3D SLAM with lidar and RGB-D sensors, ideal for mapping dynamic environments. To launch RTAB-Map:
ros2 launch rtabmap_ros rtabmap.launch.py depth:=true
This command enables depth processing, adding 3D mapping capabilities.
Optionally, you can visualize in RViz2:
ros2 run rviz2 rviz2 -d [rtabmap_config.rviz]

3. **Code for Handling SLAM Data**:
Here’s Python code for initializing SLAM nodes and managing lidar data:

import rclpy
from rclpy.node import Node
from sensor_msgs.msg import LaserScan

class LidarSLAM(Node):
    def __init__(self):
        super().__init__('lidar_slam')
        self.subscription = self.create_subscription(
            LaserScan,
            '/scan',
            self.listener_callback,
            10)

    def listener_callback(self, msg):
        # Process lidar data
        self.get_logger().info(f'Lidar data received: {msg.ranges}')

rclpy.init()
node = LidarSLAM()
rclpy.spin(node)
rclpy.shutdown()

This code creates a ROS 2 node that listens to lidar data and logs it, allowing developers to build custom SLAM algorithms on top.

4. **Saving Maps**:
Once mapping is complete, save the map data:
ros2 run nav2_map_server map_saver_cli -f /path/to/map
This command stores the generated map, enabling future use or analysis.

These code samples provide a foundation for SLAM operations on the Yahboom Jetson Orin Nano, making it easier to expand into more complex navigation tasks.

Advanced Code Optimization Techniques for ROS 2 SLAM

Optimizing SLAM in ROS 2 on the Yahboom Jetson Orin Nano ensures efficient and stable mapping, particularly when processing data-intensive tasks like 3D lidar and depth sensor data. Here are some advanced optimization techniques for enhancing SLAM performance.

1. **Adjusting QoS Profiles**:
ROS 2 Quality of Service (QoS) settings are essential in handling high-frequency data. Using the Best Effort QoS profile for lidar data ensures reduced latency, especially when high reliability isn’t crucial. Configure these QoS settings in your SLAM launch files to balance data flow and processing load.

2. **Reduce Lidar Scan Resolution**:
Lowering lidar scan resolution (by reducing point density or adjusting angular resolution) can reduce processing demands without significantly impacting SLAM accuracy. Update Cartographer or RTAB-Map configuration files with adjusted lidar parameters to optimize performance for your use case.

3. **Implement Multi-threading**:
ROS 2 nodes support multi-threading, which can handle data collection and processing in parallel. To enable this, configure the rclcpp::executors::MultiThreadedExecutor in your code:

executor = rclcpp.executors.MultiThreadedExecutor()
executor.add_node(node)
executor.spin()

This ensures efficient resource usage on the Jetson’s CPU and GPU, maximizing parallel processing power.

4. **Optimize Data Filtering**:
Implement data filtering techniques like voxel grid downsampling in RTAB-Map to reduce the size of point clouds. In RTAB-Map, add the following configuration to downsample data:
rtabmap_args "--RGBD/ProximityPathFiltering true"
Filtering point clouds reduces memory load while retaining key features for accurate mapping.

5. **Enable CUDA Acceleration**:
The Jetson Orin Nano supports CUDA, which can accelerate SLAM operations. Configure SLAM packages to utilize GPU acceleration if available, especially in RTAB-Map, which supports CUDA for faster image processing and point cloud rendering.

By applying these advanced optimizations, your Yahboom Jetson Orin Nano Robot can achieve efficient, real-time SLAM, even with complex sensor inputs and high-frequency lidar data.

Product Overview: Yahboom Jetson Orin Nano 4G ROS2 Robot with Lidar

The Yahboom Jetson Orin Nano 4G ROS2 Robot with Lidar Mapping is a high-performance robotic platform designed for both beginners and advanced robotics enthusiasts. Equipped with the NVIDIA Jetson Orin Nano 4G, this robot features a powerful AI processing capacity of 40 TOPS, which enables seamless handling of SLAM, object detection, and real-time navigation tasks in ROS 2 environments.

This Yahboom robot is built with a Mecanum wheelbase, allowing for omnidirectional movement and flexible navigation. It integrates an advanced lidar sensor for precise SLAM, providing accurate mapping of complex environments. With Docker support and pre-installed ROS2 Noetic and Ubuntu 20.04, this robot is tailored for experimenting with ROS 2 applications like RTAB-Map and Cartographer SLAM.

Priced at $2,091.95 CAD, the Yahboom Jetson Orin Nano 4G ROS2 Robot offers unmatched value with its hardware and software capabilities, making it an excellent investment for those exploring high-level robotics. With Python programming support and extensive documentation, this robot is ideal for learning, experimenting, and advancing in robotics. For anyone passionate about robotics and AI, this platform provides a perfect blend of hardware and software to push the boundaries of robotic mapping and navigation.