yolov8_ros

ROS 2 wrap for Ultralytics YOLOv8 to perform object detection and tracking, instance segmentation and human pose estimation. There are also 3D versions of object detection and human pose estimation based on depth images.

Installation

$ cd ~/ros2_ws/src
$ git clone https://github.com/mgonzs13/yolov8_ros.git
$ pip3 install -r yolov8_ros/requirements.txt
$ cd ~/ros2_ws
$ rosdep install --from-paths src --ignore-src -r -y
$ colcon build

Usage

YOLOv8

$ ros2 launch yolov8_bringup yolov8.launch.py

Parameters

• model: YOLOv8 model (default: yolov8m.pt)
• tracker: tracker file (default: bytetrack.yaml)
• device: GPU/CUDA (default: cuda:0)
• enable: wether to start YOLOv8 enabled (default: True)
• threshold: detection threshold (default: 0.5)
• input_image_topic: camera topic of RGB images (default: /camera/rgb/image_raw)
• image_reliability: reliability for the image topic: 0=system default, 1=Reliable, 2=Best Effort (default: 2)

YOLOv8 3D

$ ros2 launch yolov8_bringup yolov8_3d.launch.py

Parameters

• model: YOLOv8 model (default: yolov8m.pt)
• tracker: tracker file (default: bytetrack.yaml)
• device: GPU/CUDA (default: cuda:0)
• enable: wether to start YOLOv8 enabled (default: True)
• threshold: detection threshold (default: 0.5)
• input_image_topic: camera topic of RGB images (default: /camera/rgb/image_raw)
• image_reliability: reliability for the image topic: 0=system default, 1=Reliable, 2=Best Effort (default: 2)
• input_depth_topic: camera topic of depth images (default: /camera/depth/image_raw)
• depth_image_reliability: reliability for the depth image topic: 0=system default, 1=Reliable, 2=Best Effort (default: 2)
• input_depth_info_topic: camera topic for info data (default: /camera/depth/camera_info)
• depth_info_reliability: reliability for the depth info topic: 0=system default, 1=Reliable, 2=Best Effort (default: 2)
• depth_image_units_divisor: divisor to convert the depth image into metres (default: 1000)
• target_frame: frame to transform the 3D boxes (default: base_link)
• maximum_detection_threshold: maximum detection threshold in the z axis (default: 0.3)

Demos

Object Detection

This is the standard behavior of YOLOv8, which includes object tracking.

$ ros2 launch yolov8_bringup yolov8.launch.py

Instance Segmentation

Instance masks are the borders of the detected objects, not the all the pixels inside the masks.

$ ros2 launch yolov8_bringup yolov8.launch.py model:=yolov8m-seg.pt

Human Pose

Online persons are detected along with their keypoints.

$ ros2 launch yolov8_bringup yolov8.launch.py model:=yolov8m-pose.pt

3D Object Detection

The 3D bounding boxes are calculated filtering the depth image data from an RGB-D camera using the 2D bounding box. Only objects with a 3D bounding box are visualized in the 2D image.

$ ros2 launch yolov8_bringup yolov8_3d.launch.py

3D Object Detection (Using Instance Segmentation Masks)

In this, the depth image data is filtered using the max and min values obtained from the instance masks. Only objects with a 3D bounding box are visualized in the 2D image.

$ ros2 launch yolov8_bringup yolov8_3d.launch.py model:=yolov8m-seg.pt

3D Human Pose

Each keypoint is projected in the depth image and visualized using purple spheres. Only objects with a 3D bounding box are visualized in the 2D image.

$ ros2 launch yolov8_bringup yolov8_3d.launch.py model:=yolov8m-pose.pt