Vision, Depth, and LIDAR Pipelines for ROS 2 Robots

Source: ros2-copilot-skills catalog

Why This Matters

Perception is where robots start drowning in data. The question is rarely whether a sensor works in isolation. The real question is whether its output is shaped, filtered, framed, and timed in a way that localization, costmaps, and autonomy can trust.

Distilled Takeaways

• The useful perception pipeline is the one that produces stable downstream products, not the one with the most impressive raw sensor output.
• Depth images can feed navigation through point clouds or through virtual laser scans. The right choice depends on CPU budget, field of view, and how much 3D reasoning you actually need.
• LIDAR pipelines usually fail at the edges: bad mounting, self-hits, unfiltered spurious returns, or assumptions about scan height and obstacle semantics.
• Frame conventions matter more with optical sensors because their axis conventions differ from the robot body frame.
• Detection pipelines should be tied to the action they support: obstacle avoidance, target following, anomaly detection, mapping support, or operator awareness.

Practical Value

• Choose the simplest derived perception product that supports the task.
• Filter raw data before feeding it into costmaps whenever the environment or sensor is noisy.
• Keep camera and LIDAR frame mounting explicit in URDF and TF.
• Separate navigation-facing perception from higher-level semantic perception so each can be tuned for its own failure modes.

Start Here

• For camera geometry: Camera Calibration for ROS 2
• For depth sensing into Nav2: Depth Cameras for Navigation and Mapping
• For lidar bringup and cleanup: LIDAR Driver Bringup and Frame Alignment and LIDAR Filter Chains and Self-Hit Removal
• For 3D obstacle feeds: Point Cloud Processing for Navigation
• For semantic perception: Object Detection Pipelines in ROS 2, YOLO Integration for ROS 2 Robots, Person Tracking for Robot Behaviors, and DepthAI and OAK-D Spatial AI

Additional Perception Topics

• Transport and throughput: Compressed Image Transport in ROS 2
• Geometry-rich lidar processing: Laser Scan Processing and Filtering and Wall and Line Extraction from LIDAR
• Inspection-style AI: Visual Anomaly Detection for Robots

Corroborating References

• ROS 2 image_pipeline repository
• Nav2 concepts: environmental representation
• Gazebo and ROS 2 integration overview

When to Read the Original Source

Go to the original skills when you need exact launch patterns for depth_image_proc, depthimage_to_laserscan, LIDAR driver setup, point-cloud filtering, YOLO integration, or DepthAI-specific spatial AI workflows.