Object Detection Pipelines in ROS 2

Source: ros2-copilot-skills object detection skill

Why This Matters

An object detector is only the beginning of a perception pipeline. Real robots need to decide what a detection means, where it is in the world, how stable it is over time, and whether it should affect planning, autonomy, or operator awareness.

Distilled Takeaways

• 2D bounding boxes are usually not enough. Most robot behaviors need either a 3D position, temporal stability, or both.
• Registered depth and CameraInfo are what turn image detections into spatial detections.
• Confidence filtering and temporal smoothing are basic hygiene, not advanced features.
• Detection pipelines should be coupled to a use case: marking obstacles, triggering alerts, following a target, or populating a world model.
• Visualization helps, but the real output contract should be machine-usable topics and messages.

Practical Guidance

• Define the behavior interface first, then choose what detection outputs are actually needed.
• Use a small image patch around the box center when looking up depth instead of trusting a single pixel.
• Publish both detections and visual markers so humans and downstream logic can inspect the same state.
• Keep inference rate and camera rate separate if compute is tight.

Corroborating References

• vision_msgs package
• tf2 tutorials

When to Read the Original Source

Go to the original skill when you want concrete back-projection examples, marker publishing patterns, filtering ideas, and BT-oriented usage patterns for turning detections into actions.