Researchers at the University of Arizona have advanced 3D-sensing technology for autonomous cars, robots, and more.
This image shows the prototype in the lab. The sensor, consisting of a laser scanner and an event camera , scans the mixed reflectance scene .
Navigating a chaotic city street during rush hour is a subconscious breeze for humans. Our eyes adjust instantly to glare, shadows, and varying surfaces. For machines, however, it is a nightmare. Self-driving cars and surgical robots routinely become blinded by mixed-reflectivity surfaces.
These technologies get utterly confused by the transition from a matte brick wall to a shiny metallic bumper, or from dull tissue to glistening bodily fluids. Current 3D sensors are usually built to read one or the other, and fail when forced to look at both simultaneously. The team’s new approach allows sensors to capture images faster and in sharper detail, without getting blinded by tricky, reflective surfaces, using a laser scanner and an event camera.
“Humans already have a built-in 3D camera system – the stereo vision of our two eyes,” said Florian Willomitzer, an associate professor at the U of A Wyant College of Optical Sciences. “One of our goals is to enable computers and machines to see in 3D better than any human, which is crucial for a multitude of technological challenges, such as reliable navigation of self-driving cars, accurate guidance during robotic surgery or improved sensing capabilities in industrial inspection and biomedical imaging,” Willomitzer said.
There is a massive catch, though. To measure anything complex, the screen projecting the patterns must be enormous. Automotive manufacturers usually build tunnel-like structures lined with screens large enough to inspect aThe Arizona team found a simple way to burn down the hardware requirements. Instead of building a massive screen to project light onto a shiny object, why not turn the room itself into the screen?
“We can use a laser scanner to capture everything in the room, with whatever is inside, including objects with specular, glossy, and matte surfaces, as well as matte walls. We then use our algorithms to separate the diffuse from the specular surfaces and can eventually use all measured diffuse scene parts as a virtual screen for the deflectometry measurement of the specular parts,”Mapping a room is fine for a static lab environment, but it does not solve the issue of a speeding autonomous car or a moving surgical tool.
To make this practical, the team threw out conventional cameras.that tracks only changes in local brightness at ultra-high time resolutions. This elimination of redundant data enables the technology to easily capture high-speed, 3D video of moving objects, even in challenging environments with variations in lighting and surface reflectivity. The prototype system achieves motion-robust 3D tracking of mixed-reflectivity scenes at incredibly high frame rates. Right now, the technology is confined to a tabletop setup in a University of Arizona laboratory.
However, the architecture is fundamentally scalable. Mrigakshi is a science journalist who enjoys writing about space exploration, biology, and technological innovations. Her work has been featured in well-known publications including Nature India, Supercluster, The Weather Channel and Astronomy magazine. If you have pitches in mind, please do not hesitate to email her. Energy
3D-Sensing Technology AI Deflectometry. Inventions And Machines Physics Robotic Surgery Self-Driving Cars
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