Multi-Camera Robotics: A Comprehensive Analysis

various 3d cameras from various brands - ZED, Zivid, IDS, RealSense

This blog post aims to provide a comprehensive understanding of when and why to employ multi-camera systems in robotic applications. We show the various scenarios where multiple cameras are not just beneficial but necessary. We'll explore how these setups can enhance field of view, resolution, and robustness, and discuss the caveats and technical considerations that come with such setups. Whether it's for mobile robots, pick-and-place tasks, or complex 3D modeling, you'll get to face the question: should I add another camera ?

mir mobile robots with multiple cameras

Two MIR robots having a dual-camera setup for collision avoidance.

When Would You Use More Than One Camera?

dual camera setup for larger field of view

We identified 3 clear cases where engineers add one or more extra cameras:

  1. Enlarging the Field of View
    In scenarios like mobile robots navigating through their environments, or in automated pick-and-place tasks involving multiple bins, the need to add a camera serves to increase the Field of View in order to detect more objects or obstacles.
  2. Improving Resolution Over Field of View
    In cases where the Field of View is sufficient, but not the resolution, one can adjust the lens and/or the distance of the camera to the environment to narrow the Field of View, which increases the resolution, and add other cameras to compensate for the loss of Field of View.
  3. Enhancing Robustness in Field of View
    In environments where reflections or occlusions occur, having several viewpoints can mitigate the risk of visual information loss. This redundancy ensures that even if one camera's view is compromised, others can compensate.

Trade-offs in Multi-Camera Setups

Let’s now take a look at the issues and trade-offs you may encounter when adding one or more cameras to your system.

Choosing between CPU Processing and GPU/VPU

Multi-camera systems can quickly overload a robot's CPU due to the significant amount of data generated. This necessitates a shift towards more efficient processing methods. Utilizing GPU processing can be a more effective solution, as GPUs are better suited to handle large volumes of visual data. NVIDIA created the Isaac ROS ARGUS library to help offload some processing to the GPU. Additionally, selecting cameras that can generate depth maps internally (like the Intel Realsense) using their Visual Processing Units (VPUs) can significantly reduce the processing load on the robot's CPU.

Full Frame Rate vs. Reduced Frame Rate

The full frame rate provides more detailed and real-time visual data, which is crucial for navigation tasks. We typically update the state model of the robots environment with every camera frame in order to reduce noise and get better measurements. However, this comes at the cost of increased data processing requirements. Reducing the frame rate can alleviate the processing burden but may compromise the system's responsiveness when used in feedback loops and increase the noise levels on measurements and cost maps of the environment.

Avoiding Crosstalk in Overlapping Fields of View

In scenarios where the fields of view of multiple cameras overlap, it's crucial to prevent crosstalk, especially when using structured light 3D cameras. Active and passive stereo don't have this issue and active stereo cameras can actually benefit from crosstalk as the additional IR dot patterns show more texture in the Field of View.

Complexity in Application and Scalability Challenges

Multi-camera setups inherently increase the complexity of robotic applications. Scaling such systems can be challenging, as it often leads to trade-offs between performance, cost, and complexity. Decisions such as whether the cameras' fields of view should overlap, and the choice between using classical algorithms or neural networks for processing, add layers of complexity to the system design and implementation, leading to uncertain outcomes of certain design decisions and increased testing and trial-and-error cost.

legged robot with multiple cameras

The ANYmal Robot from ANYbotics

Essential Features for Moving Multi-Cameras

We can be quite sure that the vast majority of robotic applications that use multiple cameras will be moving/mobile robots. Just think about the fleet of self-driving vehicles, warehouse robots or agricultural robots. So here’s the must-haves for these applications.

Time-Triggered Synchronization

When the object in view moves, or the camera moves, some kind of hardware-based synchronization (by means of an electrical signal/pulse ) is needed between the cameras. The cameras can be configured in two ways: One camera serves as the master, and the others act as slaves, synchronizing their capture times based on the master camera's signals. Alternatively, an external signal generator can be used, where all cameras are configured as slaves to this signal, ensuring synchronized data capture.

Global Shutter

Cameras equipped with a global shutter are essential in scenarios where the object or the camera is in motion. Unlike rolling shutters, global shutters capture the entire image frame at once, eliminating distortions caused by movement.

Popular Camera Models for Multi-Camera Robotic Applications

We list here some cameras that are equipped with the essential features discussed earlier, making them ideal for various robotic applications.

3D Cameras

various 3d cameras from various brands - ZED, Zivid, IDS, RealSense

  • Stereolabs ZED X Variants
    • These cameras are compatible with GMSL2 capture cards, which include trigger/strobe functions. They are well-suited for multi-camera systems on NVIDIA platforms.
  • Intel Realsense Series
  • Ensenso Stereo Camera Series
    • Most Ensenso cameras are GigE cameras with a GPIO connector to trigger it externally.
  • Leopard Imaging Hawk Stereo GMSL2 Camera
    • This camera is recognized for its high-resolution stereo imaging and compatibility with GMSL2 technology and the NVIDIA Jetson platform.
  • ZIVID Two Structured Light Camera
    • Known for its precision in 3D structured light imaging, the ZIVID Two is fit for environments where detailed depth data is crucial and no motion nor FoV overlap is present.

2D Cameras

lucid vision labs triton 2d camera
  • Leopard Imaging Maxim GMSL 2 OWL Camera
    • A GMSL camera that that is supported on the NVIDIA Jeston platform.
  • Lucid Vision Labs Triton Camera
    • These GigE cameras come with trigger GPIO pins, allowing for easy integration into multi-camera systems.


There is much more to do to bring multi-camera setups into reality. We didn’t discuss yet calibration techniques, stitching the 2D or 3D images or how to minimize load on the CPU in practice, and many other considerations. If you want to discuss your camera setup with us, feel free to reach out below!

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