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5.1.1. Reducing CPU Usage with Orbbec ROS Package

You can find example usage code in the example.

This document outlines strategies for minimizing CPU usage in the OrbbecSDK_ROS2 v2 environment when using Gemini 330 series cameras. The firmware version must be no lower than 1.4.10, and device should be set to Default.

5.1.1.2. Launch Files Used for Testing

  • gemini_330_series_lower_cpu_usage.launch.py

  • multi_camera_lower_cpu_usage.launch.py

5.1.1.3. Test environment

Hardware Configuration

  • CPU: Intel i7-8700 @ 3.20GHz

  • Memory: 24 GB

  • Storage: Micron 2200S NVMe 256GB

  • GPU: NVIDIA GeForce GTX 1660Ti

  • OS: Ubuntu22.04

ROS Configuration

  • ROS Version: ROS2 Humble

  • SDK Version: OrbbecSDK_ROS2 v2.2.1

Camera Setup

  • Devices: 2x Gemini 335, 1x Gemini 336, 1x Gemini 336L

  • Firmware Version: 1.4.10

5.1.1.4. Test Setup

Stream Settings:

  • Depth / IR Left / IR Right: 848×480 @ 30fps

  • Color: 848×480 @ 30fps

Note: The following CPU usage data focuses on uvc_backend, color_format and various filter combinations.

5.1.1.5. Test Results

uvc_backend Comparison (RGB format)

libuvc CPU Usage v4l2 CPU Usage Absolute Change
182.8% 118.8% -64.0%

The CPU usage can be significantly reduced with v4l2 backend. In our implementation, v4l2 works without requiring any patches to the Linux kernel, allowing users to easily switch between v4l2 and libuvc and maintaining full compatibility with standard Linux distributions.

color_format Comparison (MJPG vs RGB)

Backend MJPG CPU Usage RGB CPU Usage Absolute Change
libuvc 347.7% 182.8% -164.9%
v4l2 170.0% 118.8% -51.2%

The CPU usage can be reduced if the RGB format is selected instead of MJPG, since the decoding of MJPG image will consume the host CPU resource.

Filter Configuration Impact

Filters Applied libuvc CPU Usage CPU Usage Increase v4l2 CPU Usage CPU Usage Increase
No Filter (benchmark) 182.8% 0.0%(benchmark) 118.8% 0.0%(benchmark)
(software)noise_removal_filter 218.0% +35.2% 128.5% +9.7%
(software)noise_removal_filter + spatial_filter 469.6% +286.8% 336.7% +217.9%
hardware_noise_removal_filter 186.3% +3.5% 115.4% -3.4%
hardware_noise_removal_filter + spatial_filter 251.3% +68.5% 152.5% +33.7%

Based on the test results, using only the hardware_noise_removal_filter results in a negligible change in CPU usage for both libuvc (+3.5%) and v4l2 (-3.4%) compared to the no-filter benchmark, as this filter runs internally on the camera hardware. In contrast, other filters execute on the host system. Adding the spatial_filter to the hardware filter leads to a moderate increase in CPU usage, while applying the software-based noise_removal_filter —either alone or combined with spatial_filter —significantly increases CPU load. To maintain low CPU usage, it is recommended to avoid software-based filters and rely solely on the hardware_noise_removal_filter.

5.1.1.6. Further Optimizationa

Parameter Recommendation Note
depth_registration false or true with align_mode=HW Software alignment consumes more CPU
enable_point_cloud false Disabling point cloud reduces CPU usage
enable_colored_point_cloud false Disabling colored point cloud reduces CPU usage