Omdia: The global industrial robot image sensor market size is approximately $200 million by 2025

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（Source: Zhihui Finance）

Zhihui Finance APP learned that the global sensor market is steadily growing, driven by autonomous driving and artificial intelligence. According to Omdia estimates, in 2025 the market size of image sensors used in industrial robots worldwide will be approximately $200 million, and it is expected to grow to nearly $380 million by 2029, with a compound annual growth rate of nearly 14%. This fully reflects the acceleration of global intelligentization. As the mass production and deployment of humanoid robots take hold and application scenarios become increasingly diverse, robots’ visual perception technologies will make significant progress, accelerating their evolution toward 3D, active sensing, multimodality, and integrated sensing and computing.

3D Vision

Traditional industrial robots only need to complete repetitive work at fixed positions, while embodied intelligence robots must be integrated into complex, dynamic environments—leading to the emergence of active visual perception. It refers to the system’s ability to proactively adjust its own behavior or optimize environmental parameters based on specific scenarios, including moving the position and viewpoint of sensors, dynamically adjusting sensor parameters in line with tasks, and so on. This requires visual sensors to have a deeper understanding of 3D space and dynamic environments. Upgrading 2D visual systems that use monocular/multicamera RGB to 3D visual systems. 3D visual sensors such as ToF, structured light, stereo binocular cameras, and medium-to-long-range LiDAR—those that can emit energy into the environment and receive echoes—have broad application prospects. The development of 3D vision is taking on a more diversified pattern. Different technical routes place emphasis on accuracy, detection distance, cost, and power consumption, and will coexist across various application scenarios for a long time.

Multimodal Perception Fusion

Single-modality sensors have inherent limitations, such as visible-light cameras failing in low-light conditions and LiDAR being unable to recognize textures. Multisensor fusion leverages the complementary advantages of different sensors to achieve a more comprehensive understanding of the environment. The underlying models of embodied intelligence, such as the VLA model, require visual sensors to deeply integrate with multiple modalities including language, touch, hearing, and force perception—so that robots can understand the surrounding environment more comprehensively and interact with it. For example, vision-based tactile sensors, such as vision-tactile perception, are becoming key technologies for dexterous robot hands. System-level packaging, such as SiP, can compactly integrate various sensors and processing components into a single module, driving systems to evolve toward being lighter and more adaptive.

Infrared Imaging and Multispectral Perception

The failure of visible-light cameras at night, in fog, smoke, and other conditions has led to the widespread adoption of infrared imaging technology. Infrared sensors emit and receive infrared light and measure the time difference of the reflected light, enabling precise identification of obstacles within 3-10 meters—making autonomous navigation and obstacle avoidance possible in dark environments. Based on the different reflection characteristics of different objects to infrared light, industrial robots can use infrared sensors to rapidly classify objects during object recognition and sorting tasks. Infrared thermal imaging can detect the infrared radiation emitted by objects themselves and can be used in fields such as night patrols, overheating detection of electrical equipment, and monitoring vital signs around the clock, becoming a key link in building sensing-capable materials.

Multispectral target detection integrates sensors such as visible light 400–700 nm, near-infrared 940 nm, mid-infrared 5.5–14.0 μm, and ultraviolet 390 nm into a single module, enabling robots to obtain multiple sets of physical information through a single interaction, thereby providing a comprehensive interpretation of object shape, color, and temperature.

Event Sensors

Conventional frame-based visual sensors are often limited by fixed frame rates, which can lead to motion blur and target loss when tracking fast-moving targets. Event-based visual sensors simulate the characteristics of human eye vision, providing feedback to the brain only when brightness or contrast changes are detected in the visual scene. Event sensors perfectly match embodied intelligent bodies’ needs for real-time, efficient information perception: they eliminate motion blur when tracking high-speed targets and can also effectively sense object characteristics in high dynamic range environments. At the same time, transmitting and processing far less data makes them more suitable for edge-computing platforms on mobile robots. Currently, event sensors are usually used together with RGB sensors, allowing them to output both color and event data simultaneously, providing a fusion solution for high-speed visual tasks.

Perception-Computing Integration

As embodied intelligence application scenarios become increasingly complex, higher demands are placed on the real-time performance, low power consumption, and high integration level of visual systems. In traditional visual systems, sensors and processors are separated, and massive amounts of data must be fully transmitted to the CPU or GPU for processing. This is a heavy burden for mobile robots. A perception-computing integrated architecture integrates sensors and computing units on the same chip, completing preprocessing such as feature extraction and target recognition at the data source, greatly reducing data latency, lowering system power consumption, and also protecting data security and privacy—clearing obstacles for embodied intelligence applications in certain privacy-sensitive scenarios. At present, perception-computing integration hardware architectures are divided into two types: computing near the sensor unit Near-sensor Computing and computing inside the sensor unit In-sensor Computing. With the development of new storage technologies, it becomes possible to build perception-storage-computation integrated intelligent imaging systems. For example, a brain-inspired visual paradigm is constructed by integrating photodetectors PD and resistive RAM RRAM.

In addition to breakthroughs at the hardware level, future sensor technologies also need to achieve deep fusion of multimodal data at the algorithm level, and establish a unified multimodal representation space. In addition, optimizing a system-level perception-action closed loop is equally crucial. Embodied intelligent systems with autonomous perception, understanding, and interaction capabilities are gradually moving from science fiction to reality.

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