yolov8_ros

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

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

$ ros2 launch yolov8_bringup yolov8.launch.py

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 Point Cloud 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 Point Cloud 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 Point Cloud 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