Portable SLAM

A ROS2 Jazzy package for SLAM using the IMU of Waveshare Sense Hat B and YDLidar.

Requirements

Hardware

• Single Board Computer: Raspberry Pi or OrangePi 5
• Waveshare Sense HAT B
• YDLidar Tmini Pro (can be switch to another YDLidar device)
• Dupont Connector

Enabling I2C for the Waveshare Sense HAT B

On Orange Pi 5 with ArmbianOS, the physical pins 3 (SDA) and 5 (SCL) for the 26-pin header are actually connected to I2C-5 controller in the RK3588 SoC, and are configured in mux mode 3.

• Add user overlay of /boot/ArmbianEnv.txt: user_overlays= rk3588-i2c5-m3
• Run sudo armbian-add-overlay ./hw/rk3588/rk3588-i2c5-m3.dts
• Reboot and recheck if there are some entries with sudo i2cdetect -y 5.
• Another alternative to list all of the i2c-bus by running ls /dev/i2c* | while read line; do id="$(echo $line | cut -d '-' -f 2)"; echo -e "\\n## Detecting i2c ID: $id"; sudo i2cdetect -y $id; done

See other dts overlay files in https://github.com/orangepi-xunlong/linux-orangepi/tree/orange-pi-5.10-rk3588/arch/arm64/boot/dts/rockchip/overlay

On RaspberryPi with Ubuntu 24.04 Server, the sensor is connected to I2C-1 follow these steps:

1. sudo raspi-config
2. Choose "3. Interface Options" -> "I5 I2C" -> select "yes".
3. Reboot and check that sudo i2cdetect -y 1 returns device addresses on 0x29, 0x48, 0x5c, 0x68, and 0x70.

Overall sudo i2cdetect -y 5 on Orange Pi 5 or sudo i2cdetect -y 1 on Raspberry Pi 4 should returns:

     0  1  2  3  4  5  6  7  8  9  a  b  c  d  e  f
00:                         -- -- -- -- -- -- -- -- 
10: -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 
20: -- -- -- -- -- -- -- -- -- 29 -- -- -- -- -- -- 
30: -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 
40: -- -- -- -- -- -- -- -- 48 -- -- -- -- -- -- -- 
50: -- -- -- -- -- -- -- -- -- -- -- -- 5c -- -- -- 
60: -- -- -- -- -- -- -- -- 68 -- -- -- -- -- -- -- 
70: 70 -- -- -- -- -- -- --     

Where

• 0x29 represents the Color recognition sensor TCS34725
• 0x48 represents the AD conversion ADS1015.
• 0x68 represents the IMU 9-axis sensor ICM-20948.
• 0x5C represents the Air pressure sensor LPS22HB
• 0x70 represents the Temperature and humidity sensor SHTC3

Add user to the group permission of i2c and dialout:

sudo usermod -aG i2c $USER
sudo usermod -aG dialout $USER

Software

• Install ROS2 Jazzy on your Ubuntu 24.04 system, see https://docs.ros.org/en/jazzy/Installation/Ubuntu-Install-Debs.html.
• Create a new ROS2 workspace: mkdir -p ~/ros2_ws_slam/src && cd ros2_ws_slam.
• Clone and install the YD Lidar SDK outside the ROS2 workspace git clone https://github.com/YDLIDAR/YDLidar-SDK.git ~/YDLidar-SDK.
• Clone the YD Lidar driver for ROS2 from the humble brunch, inside the ROS2 workspace: cd ~/ros2_ws_slam/src && git clone https://github.com/YDLIDAR/ydlidar_ros2_driver.git -b humble.
• Install robot_localization package sudo apt install ros-${ROS_DISTRO}-robot-localization

Getting Started

• Clone this repository inside the src folder.
• Build the package: colcon build --symlink-install.
• If the build is successful, source the local setup source ./install/setup.bash.
• Run the launch script ros2 launch portable_slam launch_opi5.py for Orange Pi 5 or ros2 launch portable_slam launch_opi5.py for Raspberry Pi 4B.

Implementation

The portable SLAM system is configured like following:

• EKF Configuration for robot_localization:

  • EKF: 20Hz with 0.5s timeout for stable processing
  • IMU: 20Hz to match EKF
  • High trust in IMU orientation
  • Conservative velocity estimation
  • Gravity compensation enabled

• slam_toolbox Configuration:

  • LiDAR-primary settings:

    1. Frequent scan processing 10 Hz (minimum_time_interval: 0.1)
    2. Optimized correlation parameters for scan matching
    3. Conservative loop closure parameters

  • IMU Integration:

    1. Uses filtered IMU data for orientation
    2. Quick transform updates (transform_publish_period: 0.02)
    3. Balanced angle/distance penalties

  • QoS Settings:

    1. Best effort reliability (matching YDLidar)
    2. History: Keep last 10 messages
    3. Configured through parameters file for better compatibility

Our integration strategies are:

• LiDAR as primary source for mapping (using scan matching)
• IMU for orientation and motion detection
• EKF for sensor fusion at reduced rates
• Proper message handling between components