The laser sensors currently used to detect 3D objects in the paths of autonomous cars are bulky, ugly, expensive, energy-inefficient – and highly accurate.
The laser sensors currently used to detect 3D objects in the paths of autonomous cars are bulky, ugly, expensive, energy-inefficient – and highly accurate.
These Light Detection and Ranging (LiDAR) sensors are affixed to cars’ roofs, where they increase wind drag, a particular disadvantage for electric cars. They can add around $10,000 to a car’s cost. But despite their drawbacks, most experts have considered LiDAR sensors the only plausible way for self-driving vehicles to safely perceive pedestrians, cars and other hazards on the road.
Now, Cornell researchers have discovered that a simpler method, using two inexpensive cameras on either side of the windshield, can detect objects with nearly LiDAR’s accuracy and at a fraction of the cost. The researchers found that analyzing the captured images from a bird’s-eye view rather than the more traditional frontal view more than tripled their accuracy, making stereo camera a viable and low-cost alternative to LiDAR.
“One of the essential problems in self-driving cars is to identify objects around them – obviously that’s crucial for a car to navigate its environment,” said Kilian Weinberger, associate professor of computer science and senior author of the paper, “Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving,” which will be presented at the 2019 Conference on Computer Vision and Pattern Recognition, June 15-21 in Long Beach, California.
Read more at Cornell University
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