ROS/ROS 2 package for real-time object detection and segmentation using the Ultralytics YOLO, enabling flexible integration with various robotics applications.

tracker_node	tracker_with_cloud_node

• The tracker_node provides real-time object detection on incoming ROS/ROS 2 image messages using the Ultralytics YOLO model.
• The tracker_with_cloud_node provides functionality for 3D object detection by integrating 2D detections, mask image, LiDAR data, and camera information.

ROS distro	Industrial CI	Docker
ROS Melodic
ROS Noetic
ROS 2 Humble

$ cd ~/{ROS_WORKSPACE}/src
$ GIT_LFS_SKIP_SMUDGE=1 git clone -b melodic-devel https://github.com/Alpaca-zip/ultralytics_ros.git
$ rosdep install -r -y -i --from-paths .
$ pip install pipenv
$ cd ultralytics_ros
$ pipenv install
$ pipenv shell
$ cd ~/{ROS_WORKSPACE} && catkin build

$ cd ~/{ROS_WORKSPACE}/src
$ GIT_LFS_SKIP_SMUDGE=1 git clone -b noetic-devel https://github.com/Alpaca-zip/ultralytics_ros.git
$ rosdep install -r -y -i --from-paths .
$ python3 -m pip install -r ultralytics_ros/requirements.txt
$ cd ~/{ROS_WORKSPACE} && catkin build

$ cd ~/{ROS2_WORKSPACE}/src
$ GIT_LFS_SKIP_SMUDGE=1 git clone -b humble-devel https://github.com/Alpaca-zip/ultralytics_ros.git
$ rosdep install -r -y -i --from-paths .
$ python3 -m pip install -r ultralytics_ros/requirements.txt
$ cd ~/{ROS2_WORKSPACE} && $ colcon build

NOTE: If you want to download KITTI datasets, remove GIT_LFS_SKIP_SMUDGE=1 from the command line.

tracker_node

$ roslaunch ultralytics_ros tracker.launch debug:=true

tracker_node & tracker_with_cloud_node

$ roslaunch ultralytics_ros tracker_with_cloud.launch debug:=true

tracker_node

$ ros2 launch ultralytics_ros tracker.launch.xml debug:=true

tracker_node & tracker_with_cloud_node

$ ros2 launch ultralytics_ros tracker_with_cloud.launch.xml debug:=true

NOTE: If the 3D bounding box is not displayed correctly, please consider using a lighter yolo model(yolov8n.pt) or increasing the voxel_leaf_size.

• yolo_model: Pre-trained Weights.
  For yolov8, you can choose yolov8*.pt, yolov8*-seg.pt.

  YOLOv8
  YOLOv8-seg

  See also: https://docs.ultralytics.com/models/

• input_topic: Topic name for input image.

• result_topic: Topic name of the custom message containing the 2D bounding box and the mask image.

• result_image_topic: Topic name of the image on which the detection and segmentation results are plotted.

• conf_thres: Confidence threshold below which boxes will be filtered out.

• iou_thres: IoU threshold below which boxes will be filtered out during NMS.

• max_det: Maximum number of boxes to keep after NMS.

• tracker: Tracking algorithms.

• device: Device to run the model on(e.g. cpu or cuda:0).

  <arg name="device" default="cpu"/>

  <arg name="device" default="cuda:0"/>

• classes: List of class indices to consider.

  <param name="classes" value="0, 1" value-sep=", "/> <!-- person, bicycle -->

  See also: https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco128.yaml

• result_conf: Whether to plot the detection confidence score.

• result_line_width: Line width of the bounding boxes.

• result_font_size: Font size of the text.

• result_labels: Font to use for the text.

• result_font: Whether to plot the label of bounding boxes.

• result_boxes: Whether to plot the bounding boxes.

• Subscribed Topics:
  • Image data from input_topic parameter. (sensor_msgs/Image)
• Published Topics:
  • Plotted images to result_image_topic parameter. (sensor_msgs/Image)
  • Detected objects(2D bounding box, mask image) to result_topic parameter. (ultralytics_ros/YoloResult)

    std_msgs/Header header
    vision_msgs/Detection2DArray detections
    sensor_msgs/Image[] masks
    

• camera_info_topic: Topic name for camera info.
• lidar_topic: Topic name for lidar.
• yolo_result_topic: Topic name of the custom message containing the 2D bounding box and the mask image.
• yolo_3d_result_topic: Topic name for 3D bounding box.
• cluster_tolerance: Spatial cluster tolerance as a measure in the L2 Euclidean space.
• voxel_leaf_size: Voxel size for pointcloud downsampling.
• min_cluster_size: Minimum number of points that a cluster needs to contain.
• max_cluster_size: Maximum number of points that a cluster needs to contain.

• Subscribed Topics:
  • Camera info from camera_info_topic parameter. (sensor_msgs/CameraInfo)
  • Lidar data from lidar_topic parameter. (sensor_msgs/PointCloud2)
  • Detected objects(2D bounding box, mask image) from yolo_result_topic parameter. (ultralytics_ros/YoloResult)

    std_msgs/Header header
    vision_msgs/Detection2DArray detections
    sensor_msgs/Image[] masks
    

• Published Topics:
  • Detected cloud points to /detection_cloud topic. (sensor_msgs/PointCloud2)
  • Detected objects(3D bounding box) to yolo_3d_result_topic parameter. (vision_msgs/Detection3DArray)
  • Visualization markers to /detection_marker topic. (visualization_msgs/MarkerArray)

ROS Melodic

$ docker pull alpacazip/ultralytics_ros:melodic
$ docker run -p 6080:80 --shm-size=512m alpacazip/ultralytics_ros:melodic

ROS Noetic

$ docker pull alpacazip/ultralytics_ros:noetic
$ docker run -p 6080:80 --shm-size=512m alpacazip/ultralytics_ros:noetic

ROS 2 Humble

$ docker pull alpacazip/ultralytics_ros:humble
$ docker run -p 6080:80 --shm-size=512m alpacazip/ultralytics_ros:humble

ROS Melodic

$ roscd ultralytics_ros && pipenv shell
$ roslaunch ultralytics_ros kitti_predict_with_cloud.launch
$ cd ~/catkin_ws/src/ultralytics_ros/rosbag && rosbag play kitti_2011_09_26_drive_0106_synced.bag --clock --loop

ROS Noetic

$ roslaunch ultralytics_ros kitti_tracker_with_cloud.launch
$ cd ~/catkin_ws/src/ultralytics_ros/rosbag && rosbag play kitti_2011_09_26_drive_0106_synced.bag --clock --loop

ROS 2 Humble

$ ros2 launch ultralytics_ros kitti_tracker_with_cloud.launch.xml
$ cd ~/colcon_ws/src/ultralytics_ros/ros2bag && ros2 bag play kitti_2011_09_26_drive_0106_synced --clock --loop