Key Strategic Insights

1. 3D Vision Becomes a Baseline Capability

2D vision is no longer sufficient for many robotic tasks. Applications such as navigation, bin picking, manipulation and collision avoidance increasingly rely on depth information.

3D vision is implemented through a mix of stereo cameras, structured light, ToF sensors and multi-camera setups. The choice depends on range, accuracy and environmental conditions rather than on a single dominant technology.

What this requires

• Clear definition of depth accuracy and range requirements
• Careful selection of sensor technology for the operating environment
• Sufficient processing resources to handle depth data in real time

2. Edge AI Shifts Intelligence Closer to the Camera

Vision workloads are moving closer to the edge. Instead of streaming raw images to a central controller, processing increasingly happens in-camera or directly on the robot controller.

This reduces latency, lowers bandwidth requirements and improves scalability, especially in multi-camera systems.

What this requires

• Cameras or controllers with integrated AI acceleration
• Well-defined interfaces between vision hardware and algorithms
• Careful partitioning of workloads between camera and robot compute

3. Algorithms Become the Real Differentiator

As camera hardware becomes more standardised, differentiation shifts to vision algorithms. Object detection, tracking, segmentation and inspection quality increasingly depend on software rather than sensor resolution alone.

This makes algorithm portability, training pipelines and long-term software maintenance critical factors in vision system design.

What this requires

• Stable and well-supported vision APIs
• Compatibility between camera output formats and AI frameworks
• Long-term support strategies for deployed algorithms

4. SWIR Gains Relevance in Inspection and Security

Short-wave infrared (SWIR) imaging is gaining traction beyond niche applications. Its ability to reveal material properties, penetrate certain substances and operate under challenging lighting conditions makes it attractive for inspection, quality control and security-related robotics.

Although still more specialised and costly than visible or NIR imaging, SWIR is increasingly considered where standard vision fails.

What this requires

• Careful evaluation of cost versus application benefit
• Integration of specialised sensors and optics
• Alignment between sensor choice and processing capabilities

5. Vision Systems Become Modular and Scalable

Robotic vision is evolving toward modular architectures: interchangeable cameras, standardised interfaces and reusable software blocks. This enables easier scaling from prototypes to full deployments and across different robot platforms.

What this requires

• Standardised camera interfaces and data formats
• Clear separation between hardware, ISP and algorithm layers
• Validation strategies that support reuse and scalability