A recent article in R&D magazine reports on the development of a low cost video camera – based driverless vehicle navigation system. Ryan Wolcott, a University of Michigan doctoral candidate in computer science and engineering, estimates that it could shave thousands of dollars from the cost of these vehicles.
The technology enables them to navigate using a single video camera, delivering the same level of accuracy as laser scanners at a fraction of the cost. His paper detailing the system recently was named best student paper at the Conference on Intelligent Robots and Systems in Chicago.
His system builds on the navigation systems used in other self-driving cars that are currently in development, including Google’s vehicle. They use three-dimensional laser scanning technology to create a real-time map of their environment, then compare that real-time map to a pre-drawn map stored in the system. By making thousands of comparisons per second, they’re able to determine the vehicle’s location within a few centimeters.
Wolcott’s system uses the same approach, with one crucial difference—his software converts the map data into a three-dimensional picture much like a video game. The car’s navigation system can then compare these synthetic pictures with the real-world pictures streaming in from a conventional video camera.