With the rapid advancement of machine vision, artificial intelligence, and human-computer interaction, robots that once existed only in sci-fi movies are now becoming a reality. Consumer-grade robots are slowly making their way into supermarkets and homes, helping us with physical tasks. But recently, some users have shared videos online showing their robotic vacuum cleaners behaving in ways that could be described as less-than-smart. This has sparked quite a bit of discussion among netizens about the current state of robotics.
Check out this funny video of a robot acting up:
It's easy to imagine how embarrassing it could be for a company if their robots malfunctioned in public due to technical issues. Product recalls, changes to production lines, and damage to brand reputation would follow, leading to a significant loss of trust.
However, the widespread attention from both the public and media has highlighted the immense potential of the robotics market. For robot manufacturers, it’s clear that choosing the right machine vision technology is crucial to enhancing product performance and improving user experience.
In the robotics industry, the current standard for intelligent recognition involves processing 2D images using algorithms. While effective, 2D images inherently lack depth information. Even with sophisticated algorithms, the limited input data can hinder overall AI capabilities like navigation, path planning, and object recognition. To address this, integrating comprehensive 3D information could significantly enhance object recognition by capturing detailed contours and physical properties, boosting AI performance across the board.
Selecting a 3D depth camera as part of the machine vision system allows for simultaneous SLAM mapping, obstacle avoidance, target tracking, human-computer interaction, and automated grasping. This approach balances product performance with cost-effectiveness, earning it industry-wide recognition as a practical and affordable solution.
Currently, there are three main types of 3D depth camera technologies:
1) **Structured Light**: Projects specific optical patterns onto objects, capturing changes in the reflected light to calculate position and depth, reconstructing the full 3D space.
2) **TOF (Time of Flight)**: Measures the time taken for near-infrared light to travel from emitter to receiver to determine object distance.
3) **Stereo System**: Uses two cameras to triangulate distances based on object images.
As you can see from the diagram below, structured light stands out due to its mature technology, high-resolution depth imaging, and affordability. Despite requiring less complex algorithms, advancements continue to push its boundaries, including improvements in anti-interference capabilities. Major tech giants like Apple, Microsoft, and Intel have embraced structured light in their products, underscoring its industry-leading advantages.
Following Apple's decision to stop licensing PremeSense and Intel's RealSense being costly, only one domestic company has successfully developed cost-effective, mass-produced structured light solutions. Their Astra series of depth cameras—Astra, Astra Pro, and Astra mini—can handle SLAM mapping, obstacle avoidance, target tracking, human-computer interaction, and automated object picking.
Watch the Obi Vision Robot Solution Video here: [Link](https://v.qq.com/x/page/y0540sz3r2v.html)
The Astra series depth cameras consist of an infrared camera, a laser projector, and an RGB camera. The module dimensions are 165mm x 38mm x 30mm with a detection range of 0.6-8 meters, offering accuracy within ±1mm to ±3mm. The viewing angle is 58.4 degrees, and the resolution can reach up to 1280x1024@7FPS. The Astra mini is even more compact at 80mm x 20mm x 19.3mm, maintaining the same detection range and accuracy but offering greater versatility.
The company also develops its core 3D computing chips, depth algorithms, and provides complete SDK support. Since its inception, they’ve filed nearly 300 patents. With a robust R&D team covering everything from chip design to deep learning algorithms, system frameworks, and application layers, they’re able to offer more cost-effective consumer-grade solutions compared to imported chips.
Recently, Obi Vision is set to release a new generation of compact structured light 3D sensors with higher resolution and lower power consumption. These cameras will cater to a wide range of devices, including robots, smartphones, tablets, and notebooks, undoubtedly influencing the design of smart terminals moving forward.
Obi Vision’s continuous progress in structured light 3D sensing technology has already seen applications in areas like interactive TVs, robotics, and security. Combining software and hardware expertise, Obisoft aims to enable every terminal device to “see†the world as humans do.
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